A review of segmentation methods in short axis cardiac MR images

Survey Paper

A review of segmentation methods in short axis cardiac MR images

Caroline Petitjean a ,?,Jean-Nicolas Dacher b

a Universitéde Rouen,LITIS EA 4108,BP 12,76801Saint-Etienne-du-Rouvray,France

b

University Hospital of Rouen,Department of Radiology &University of Rouen,INSERM U644,76031Rouen,France

a r t i c l e i n f o Article history:

Received 4May 2010

Received in revised form 23October 2010Accepted 15December 2010

Available online 24December 2010Keywords:Cardiac MRI

Segmentation

Deformable models Survey

a b s t r a c t

For the last 15years,Magnetic Resonance Imaging (MRI)has become a reference examination for cardiac morphology,function and perfusion in humans.Yet,due to the characteristics of cardiac MR images and to the great variability of the images among patients,the problem of heart cavities segmen-tation in MRI is still open.This paper is a review of fully and semi-automated methods performing segmentation in short axis images using a cardiac cine MRI sequence.Medical background and speci?c segmentation dif?culties associated to these images are presented.For this particularly complex seg-mentation task,prior knowledge is required.We thus propose an original categorization for cardiac segmentation methods,with a special emphasis on what level of external information is required (weak or strong)and how it is used to constrain segmentation.After reviewing method principles and analyzing segmentation results,we conclude with a discussion and future trends in this ?eld regarding methodological and medical issues.

ó2010Elsevier B.V.All rights reserved.

1.Introduction

Cardiovascular diseases are the leading cause of death in Wes-tern countries (Allender et al.,2008;Lloyd-Jones,2010).Diagnosis and treatment follow-up of these pathologies can rely on numer-ous cardiac imaging modalities,which include echography,CT (computerized tomography),coronary angiography and cardiac MRI.Today recognized as a reference modality for the non-invasive assessment of left ventricular function,MRI also supplies accurate information on morphology,muscle perfusion,tissue viability or blood ?ow,using adequate protocols.The cardiac contractile func-tion can be quanti?ed through ventricle volumes,masses and ejec-tion fraction,by segmenting the left (LV)and right (RV)ventricles from cine MR images.Manual segmentation is a long and tedious task,which requires about 20min per ventricle by a clinician.Be-cause this task is also prone to intra-and inter-observer variability,there has been a lot of research work about automated segmenta-tion methods.In particular,commercial software packages such as MASS (Medis,Leiden,The Netherlands)(van der Geest et al.,1994)and Argus (Siemens Medical Systems,Germany)(O’Donnell et al.,2006)are today available for automatic ventricle delineation.Even though processing time has been greatly reduced,the provided contour detection still needs to be improved to equal manual contour tracing (Fran?ois et al.,2004;Mahnken et al.,2006).While reviews on cardiac image analysis (Suri,2000;Frangi et al.,2001)and medical image segmentation (Suri et al.,2001;Pham et al.,2000)exist,we have focused on methods dedicated to cardiac MR segmentation:the particular shape of both ventricles,as well as MR characteristics,have required speci?c developments.De-spite more than 15years of research,the problem is still open,as shown by the holding of a segmentation contest on the LV in 2009during the MICCAI conference 1,and remains completely un-solved for the RV.

In the present paper,we will review automatic and semi-automatic segmentation methods of cine MR images of the cardiac ventricles,using the short-axis view,the most common imaging plane to assess the cardiac function.We wish to provide the reader with (i)major challenges linked to this segmentation task,(ii)a state-of-the art of cardiac segmentation methods,including debut methods and current ones,and (iii)future trends in this ?eld.This paper is intended for researchers in the ?eld of cardiac segmenta-tion,but also to image processing and pattern recognition researchers interested to see how different segmentation tech-niques apply for a given application.The remaining of the paper is as follows.Short-axis MR images and the challenge of their seg-mentation are presented in Section 2.In Section 3,a categorization for segmentation methods is proposed and justi?ed.Segmentation methods are presented in Section 4and their results are discussed

1361-8415/$-see front matter ó2010Elsevier B.V.All rights reserved.doi:10.1016/j.media.2010.12.004

?Corresponding author.Tel.:+33232955215;fax:+33232955022.

E-mail addresses:Caroline.Petitjean@univ-rouen.fr (C.Petitjean),Jean-Nicolas.Dacher@chu-rouen.fr (J.-N.Dacher).

1

MICCAI Workshop –Cardiac MR Left Ventricle Segmentation Challenge,http://smial.sri.utoronto.ca/LV_Challenge/,2009.

in Section 5.At last conclusion and Section 6.

2.Cardiac MR image processing 2.1.Description of short-axis MR images

The complexity of segmenting heart chambers mainly relies on heart anatomy and MRI LV function consists in pumping the oxygenated and consequently to the systemic circuit.The LV known shape of ellipsoid (Fig.1)and is dium,whose normal values for thickness range On the contrary,the RV has a complex crescent lower pressure to eject blood to the lungs and times thinner than the LV,reaching the limit of tion.For those reasons,and because its function the LV’s,most research effort has focused on MRI has proven to provide an accurate (Shors et al.,2004).

Let us now describe the data that compose a examination.The standard imaging plane is long (apex-base)axis and called short axis of the heart in MRI covers the whole organ short-axis slices,distance between two from 10to 20mm.As the heart is a moving quired throughout the cardiac cycle,thanks to of MR acquisition to the ECG signal and the namic)MR sequences.In full-size images,the quite a small surface in cardiac MR images and ally restricted to a smaller region of interest phases (or images)can be obtained during one the currently available equipment,yielding a of about 30ms.The number of phases decreases proportionally of gray levels or structure shapes.Gray level intensities can also differ due to the use of different MRI scans or different bFFE se-quences.Fuzziness can be observed on some parts of the images,mostly due to blood ?ow and partial volume effects,aggravated by respiration motion artefacts.This former effect is a consequence of non-zero thickness of MRI slices:in some areas,a voxel can be a mixture of several tissue types.In terms of shape,the ventricle var-ies over patients,over time and over the long axis.This variability must be accounted for in segmentation algorithms.

2.2.Issues in cardiac MR image segmentation

Segmentation of the heart on these images consists in delineat-ing the outer wall,also called epicardium and the inner wall,called endocardium (Fig.2).Each contour to be delineated presents spe-ci?c segmentation dif?culties,as described below.

Epicardium segmentation.The epicardial wall is at the frontier between the myocardium and surrounding tissues (fat,lung),which have different intensity pro?les and show poor contrast with the myocardium.Segmentation of the epicardial wall is thus dif?cult,especially for the RV because of its reduced thickness.Endocardium segmentation.Endocardium surrounds the LV cav-ity.MRI provides quite good contrast between myocardium and the blood ?ow without the need of contrast medium.Still segmen-tation dif?culties exist,that mostly originate from gray level inho-mogeneities in the blood ?ow,and particularly because of the presence of papillary muscles and trabeculations (wall irregulari-ties)inside the heart chambers,which have the same intensity pro-?le as the myocardium (Fig.2).They can thus prevent from clearly delineating this wall.According to clinical standards,they should not be taken into account for endocardial wall segmentation.

Fig.1.LV and RV geometry.

Fig.2.A full size short-axis cardiac MR image and a ROI identifying the heart.

Fig.3.Variability among cardiac images.

170 C.

Because the endocardial wall is less dif?cult to segment that epicardial one,and since it is the only contour required to ventricular volume,some works only focus on the segmentation.

Position along the apex-base axis.Segmentation complexity depends on the slice level of the image.Apical and basal images are more dif?cult to segment than mid-ventricular Indeed,MRI resolution is not high enough to resolve size of structures at the apex and ventricle shapes are strongly close to the base of the heart,because of the vincinity of the Note also that the RV shape varies much throughout the apex-base axis,whereas the LV remains close to a ring shape,as shown in Fig.4.

2.3.Quanti?cation of cardiac systolic function

Endocardial and epicardial contours are used to quantify the heart function.Physicians are especially interested by the compu-tation of the RV and LV mass and volume at two precise moments of the cardiac cycle:the time of greatest contraction (end systole,ES)and the time of maximum ?lling (end diastole,ED)(Fig.5).Vol-ume is obtained by integrating surfaces obtained on the endocar-dium only,whereas mass computation requires the integration of both endocardial and epicardial surfaces.Volumes at end systole and end diastole are then compared by computing the so-called ejection fraction,de?ned as the ratio of the difference in heart volume during ED and ES times to the ED time.The computation of clinical parameters thus only requires contours at two instants of the cardiac cycle.Full cycle segmentation of the ventricles,although not used currently in clinical routine,allows to assess temporal volume and mass variations and would be of great inter-est as an indicator of cardiac performance (Caudron et al.,2011).It also allows to compute endocardial and epicardial wall motion,as well as myocardium thickening.Note that,because of the aper-ture problem,intramyocardial motion cannot be analyzed from standard cine-MR images but requires dedicated modalities such as tagged MRI (Rougon et al.,2005).3.Overview of segmentation methods

For this review,we have been considering the papers among peer-reviewed publications,that included (i)a segmentation categorization can be used with a few nuances and what criteria is chosen to classify the different methods.

3.1.A categorization of short-axis MR image segmentation methods Numerous segmentation problems require the use of a priori knowledge so as to increase their robustness and accuracy.On one hand,user interaction,an important consideration in any seg-mentation problem (Pham et al.,2000),can be regarded as a pri-ori knowledge.In our application,it can consist in either pointing the center of the LV cavity or manually tracing the ventricle bor-der.These two levels of user interaction do not have the same im-pact in terms of reproductibility,neither require the same level of expertise,and are thus going to be distinguished.On the other hand,as medical expertise is available on cardiac ventricles,it can be used during the segmentation process,with different lev-els of information as well.Prior information can consist in weak assumptions such as simple spatial relationships between objects (for instance,the RV is to the left of the LV)or anatomical assumptions making use of the circular geometry of the LV.The knowledge of the heart biomechanics can also be integrated into the segmentation process,to constrain adequately the segmenta-tion of the different phases.More accurate information regarding the shape of the heart can also be obtained through higher-level information,such as a statistical shape model.On some images,especially at the apex,myocardium borders are very fuzzy and ill-de?ned,and it is very dif?cult to rely on the image alone to perform segmentation.The use of a shape model for example is thereby particularly useful,but at the expense of building a train-ing data set with manually generated segmentations.We will thus distinguish three main levels of information used during Fig.

4.Cardiac images corresponding to 12short-axis slices from apex to base.

Cardiac MR images at end diastole (left)and end systole C.Petitjean,J.-N.Dacher /

including snakes and their variants,on the contrary,offer a great, versatile framework for using either weak or strong prior.They can include anatomical information,as well as high level informa-tion,in the so-called shape prior based segmentation framework, or through active shape and appearance models(ASM/AAM).At last,atlas guided segmentation also make use of a set of manually segmented images.Considering both the usual segmentation method categorization and the level of external information,we propose the following two main categories(Table1):

segmentation based on no or weak prior,that includes image-based and pixel classi?cation based methods,as well as deform-able models;

segmentation based on strong prior,that includes shape prior based deformable models,active shape and appearance models, and atlas based methods.

Methods combining strong and weak priors have been catego-rized in the strong prior section.In the next section,comparison criteria for segmentation methods are detailed.

3.2.Points of comparison for segmentation methods

Segmentation methods are compared in Table2,on the basis of their experimental conditions.Notations and acronyms used in this table are explained below.

LV/RV.This?ag indicates whether segmentation results are pro-vided on both LV(resp.RV)epicardial and endocardial contours (LV,RV),or endocardial only(LVv,RVv).

User interaction(U).User interaction may be used for segmenta-tion initialization.We will distinguish between limited user inter-action(U1),such as pointing the LV center or dragging a circle,and

Table1

Categorization of segmentation methods vs.type of prior information and related sections in the paper.

Region-and edge-based Pixel classi?cation Deformable models ASM/AAM Atlas

No prior Section Section Section

Weak prior 4.2.1 4.2.2 4.2.3

Strong prior Section Section Section

4.3.1 4.3.2 4.3.3

Table2

Overview of cardiac MR segmentation methods presented in70papers.LV/RV:Left and/or right ventricle segmentation,U:User interaction,E:External information,M:Motion information,ASA:Assessment of segmentation accuracy.Please refer to the text(Section3.2)for other acronym meaning.

Authors Basic method principle LV/RV U E M ASA

Segmentation with weak or no prior

(48)

Image-based Gupta et al.(1993)DP LV U2AM P–

Geiger et al.(1995)DP LV U2–P–

van der Geest et al.(1994)DP LV U1AM P EF V

M

Goshtasby and Turner(1995)Thresholding LVv

RVv

A AM–S

Kaushikkar et al.(1996)Thresholding LVv U1––EF V

Weng et al.(1997)Thresholding LVv

RVv

U1–––

Nachtomy et al.(1998)Thresholding LV U1AM–EF V

M

Waiter et al.(1999)Gradient LV U1––EF

Lalande et al.(1999)DP LV U1AM–V

Fu et al.(2000)DP LV U1––P2C

Cassen et al.(2001)Split and merge LV U2–P V

Noble et al.(2003)Non rigid registration LV U2–P EF V

Yeh et al.(2005)DP LVv U1AM–V

Liu et al.(2005)DP RVv–AM––

üzümcüet al.(2006)DP LV U2–M EF V P2C

Katouzian et al(2006)Thresholding LV RVv U1––S

Lin et al.(2006)Thresholding LVv A AM P P2C

Lee et al.(2008)Graph searching LV U1AM–EF V

M

Jolly et al.(2009)Shortest path and minimum

surf.

LV A AM P S P2C

Cousty et al.(2010)MM,watershed transform LV U1AM M EF M P2S

Pixel classif.Boudraa(1997)Clustering(FCM)LVv–AM–EF

Stalidis et al.(2002)Neural networks LV U1–M P2S

Gering(2003)GMM,MRF LV RVv A AM––

Lynch et al.(2006a)Clustering(K-means)LV–AM–EF S P2C

Kedenburg et al.(2006)Fuzzy kNN and graphcut LV A AM–V

Pednekar et al.(2006)GMM and DP LV A AM P EF V

Cocosco et al.(2008)Clustering LVv

RVv

A AM–EF V

Deformable

models

Ranganath(1995)Active contours LVv U1–P EF S

Chakraborty et al.(1996)Active contours+region term LV U1–––

Yezzi et al.(1997)GAC LV U1––V

Pham et al.(2001)3D deformable surfaces+GVF LV U1BM P V

Paragios(2002)GAC+GVF+region term LV U1AM––

172 C.Petitjean,J.-N.Dacher/Medical Image Analysis15(2011)169–184

advanced user interaction(U2),that consists in manually segment-ing the?rst image of the sequence.When no user interaction is needed,most methods start with an automatic localization of the heart(A).Methods for which no user interaction nor automatic heart localization is reported are speci?ed with a hyphen(-).

Use of external information(E).External information incorporated during the segmentation process may be a weak prior,that can be based on anatomical modeling(AM)such as(i)the circular aspect of the LV:transformation into polar coordinates,use of radial lines, (ii)simple spatial relationships,e.g.the RV is positioned on the left side of the LV,or(iii)the use of an ellipsoid,cylinder or bullet-shaped volumetric model.Assumptions based on the heart motion and deformations,such as the myocardium?ber stiffness,can be integrated into a biophysical model(BM).A strong prior(SP), namely a set of manually drawn borders gathered and synthetized into a statistical model,may also be used.Methods making no use of external information at all are speci?ed with a hyphen(-).

Use of motion information(M).As the heart is a moving organ,its motion can be taken into account in the segmentation process.We are not interested in the underlying contour point tracking prob-lem,that would consist in recovering the trajectories of material points,but rather by how the time dimension can help the seg-mentation process(Montagnat and Delingette,2005).We will distinguish between approaches that propagate an initial segmen-tation result(P)on the whole cardiac cycle by repeating their algorithm on each image,and approaches that explicitely take motion into account(M).Methods that do not make use of heart motion at all are speci?ed with a hyphen(-).

Assessment of segmentation accuracy(ASA).The performance of a segmentation method is quanti?ed through validation against a ground truth.Gold standard in this domain is manual delineation by an expert.Although qualitative visual evaluation is sometimes provided in certain studies(speci?ed with a hyphen),segmenta-tion accuracy is usually assessed by computing and comparing quantitative measures such as ventricle surface(S),volume(V) and mass(M),and ejection fraction(EF)based on contours ob-tained manually and automatically.Note that because of error compensation,these measures do not totally ensure accurate error measurement.In order to compare contours(resp.surfaces),error accuracy is better estimated by computing the mean perpendicular distance between them,denoted as point-to-curve(P2C)error (resp.point-to-surface(P2S)error).Correlation between manual tracing and automatic method,as well as inter-expert variability may be estimated through linear regression and Bland–Altman analysis.This latter analysis allows for comparison between two measurements by plotting the difference between the two mea-surements against their mean(Bland and Altman,1986).

We shall now review segmentation methods and explain how issues presented in Section2.2are handled,with a?rst focus on how the heart is roughly localized in the MR image.

Table2(continued)

Authors Basic method principle LV/RV U E M ASA

Zhukov et al.(2002)3D deformable surfaces LV U1AM––

Papademetris et al.(2002)Shape based matching LV U1BM P–

Santarelli et al.(2003)Active contours+GVF LV U2–P V M

Battani et al.(2003)GAC RVv U1––V

Pluempitiwiriyawej et al.

(2005)

Active contours+region term LV RV U1AM P S

Heiberg et al.(2005)3D Active contours LV U1AM P V

Wang and Jia(2006)Active contours+GVF LV U1AM P P2C

Jolly(2006)GMM+Active

contours+splines

LV U1AM P P2C

Hautvast et al.(2006)Discrete active contours LV RVv U2–P EF,V P2C

Gotardo et al.(2006)Active contours+Fourier desc.LV U1–P–

Sermesant et al.(2006)Registration LV RV U2BM P–

Yan et al.(2007)Registration LV RV U2BM P–

El Berbari et al.(2007)MM+Active contours+GVF LV U1AM–S P2C

Lynch et al.(2008)Level sets+temporal def.

model

LV A AM M P2C

Billet et al.(2009)Deformable models LV RV U2BM P–

Ben Ayed et al.(2009)Level sets+overlap priors LV U2AM–S Segmentation with strong prior(22)Shape prior Paragios et al.(2002)Level sets and stochastic

repres.

LV–SP––

Tsai et al.(2003)PCA and energy min.LVv–SP––

Kaus et al.(2004)PCA and energy min.LV–SP–P2S

Sénégas et al.(2004)Fourier desc.+Bayesian

approach

LV RVv–SP M P2C

Sun et al.(2005)PCA+Bayesian approach LV U2SP M–

Lin et al.(2005)Probabilistic map+Graphcut LV–SP–P2C

Lynch et al.(2006b)Level sets+PDF LV U1SP–S P2C

ASM/AAM Mitchell et al.(2000)AAM LV A SP–P2C

Stegmann and Nilsson(2001)AAM LV–SP–S P2C

Mitchell et al.(2001)Hybrid ASM/AAM LV RVv A SP–S P2C

Lelieveldt et al.(2001)2D+time AAM LV A SP M S P2C

Mitchell et al.(2002)3D AAM LV A SP–V M P2C

Ordas et al.(2003)ASM with IOF LV RV–SP–P2C

Stegmann and Pedersen

(2005)

3D bi-temporal AAM LV–SP–EF V

van Assen et al.(2006)3D ASM LV–SP–V P2C

Abi-Nahed et al.(2006)ASM+Robust Point Matching RVv–SP–P2C

Zambal et al.(2006)2D AAM+3D ASM LV–SP–P2S

Zhang et al.(2010)AAM+ASM LV RVv U2SP–V M P2S

Atlas Lorenzo-Valdés et al.(2002)Anatomical atlas+NRR LV RVv–SP P V

Lorenzo-Valdés et al.(2004)Probabilistic atlas+EM+MRF LV RVv–SP P V P2C

L?tj?nen et al.(2004)Probabilistic atlas+NRR LV RV–SP–P2S

Zhuang et al.(2008)Anatomical atlas+NRR LV RVv–SP–V P2C

C.Petitjean,J.-N.Dacher/Medical Image Analysis15(2011)169–184173

4.Ventricle segmentation in cardiac MRI

4.1.Automatic localization of the heart

As mentioned above,a ROI centered on the heart is generally extracted from the original MR image,in order not to process the whole image,thus decreasing computational load.Automatic ap-proaches are twofold:time-based approaches,that take advantage of the fact that the heart is the only moving organ in the images,or object detection techniques.They both have in common the use of the Hough transform,that allows to detect the position of the LV thanks to its circular aspect.Note that works presented in(Cocosco et al.,2004;Huang et al.,2007;Jolly,2008)are entirely devoted to this preliminary step,highlighting its importance.

When using time dimension,a difference or a variance image can be computed over all the data.In(Pednekar et al.,2006),a dif-ference image is computed between two images where the heart is the largest,i.e.in basal slices of the LV and at end diastole.The resulting image contains a circular region around the LV bound-aries,which is detected thanks to a Hough transform.Image differ-ence is also used in(Huang et al.,2007)followed by texture analysis and K-means clustering to isolate the heart region.Works proposed in(Cocosco et al.,2004;Gering,2003)rely on variance computation.Starting from a3D+t original image,variability along the time dimension is assessed with standard deviation of each voxel.The maximum intensity of this resulting3D image is pro-jected onto a2D image,binarized using Otsu’s method(Otsu, 1979),and dilated several times.The convex hull of the?nally ob-tained region is the?nal2D ROI(Fig.6).Some authors found that the variance image can be quite noisy and have thus proposed a method based on a Fourier analysis of the image(Lin et al.,2006; Jolly,2008).A pixelwise Fourier transform of the image sequence a Markov chain.The system is trained using positive and negative examples.The detection stage provides a cluster of pixels classi?ed as left ventricle and the?nal choice among these candidates is made using a Hough based voting procedure on the individual pro-?les,allowing for sketching a circle roughly positionned on the LV. The method proposed in(Pavani et al.,2010)is based on a popular face detection approach(Viola and Jones,2001),that consists in a Haar description of the subwindows and a cascade of adaboosted classi?ers.Authors suggest to optimize Haar-like features for a gi-ven object detection problem by assigning optimal weights to its different Haar basis functions.

4.2.Segmentation with weak or no prior

This section gathers segmentation methods with weak or no prior,including image-based,pixel classi?cation-based and deformable models.

4.2.1.Image-based methods

As seen in Section2.2,endocardial and epicardial contours pres-ent speci?c segmentation dif?culties.Many image-based methods propose to process them differently and separately,hence the focusing of certain methods on the LV endocardium only.The?rst step consists in?nding the endocardial contour,usually with thresholding(Goshtasby and Turner,1995;Weng et al.,1997; Nachtomy et al.,1998;Katouzian et al,2006)and/or dynamic programming(DP)(Gupta et al.,1993;van der Geest et al.,1994; Geiger et al.,1995;Lalande et al.,1999;Fu et al.,2000;Yeh et al.,2005;Liu et al.,2005;üzümcüet al.,2006).DP applied to image segmentation consists in searching for the optimal path in a cost matrix that assigns a low cost to object frontiers.Here,the circular geometry of the LV is taken advantage of using polar coor-

Automatic computed ROI from six patients(from(Cocosco et al.,2004),with kind permission from Elsevier). 174 C.Petitjean,J.-N.Dacher/Medical Image Analysis15(2011)169–184

posed,in order not to include papillary muscles:computation of the convex hull of the contour(van der Geest et al.,1994;Lin et al.,2006),applications of mathematical morphology(MM)oper-ations such as opening and closing on the contour(Nachtomy et al., 1998;Cousty et al.,2010),or?tting a parametric curve to the con-tour,allowing it to be smooth(Waiter et al.,1999).

The epicardium is found during a second step,often relying on the endocardial contour,that makes use of a spatial model incorpo-rating myocardial thickness or MM operators,applied on the endo-cardial contour.Note that for their initialization,most of these methods rely on a simple user intervention,such as pointing the center of the LV or dragging circles next to the myocardium contours.

When contours are processed identically,the segmentation pro-cess is initialized with manually drawn borders on the?rst image (Cassen et al.,2001;Noble et al.,2003;üzümcüet al.,2006).Man-ual contours are then propagated through all subsequent images, using split-and-merge(Cassen et al.,2001),non-rigid registration (Noble et al.,2003),or multidimensional DP:in(üzümcüet al., 2006)the initial contours provided by the user are resampled to 32landmarks.Each sample is tracked separately over time by de?ning a search space around each landmark.Cost hypercubes are then?lled with image-feature derived cost function values. The optimal connected path is tracked through cost hypercubes using DP.

4.2.2.Pixel classi?cation based methods

In medical image segmentation,pixel classi?cation is mostly used when multiple images of the same scene are available,as for instance with multispectral MRI or multimodality imaging, e.g.PETscan images(Dawant and Zijdenbos,2000).Each voxel can be described by several complementary features,and is consid-ered as an item to be classi?ed into a single class among several. The image is partitioned into regions or classes,composed of pixels that have close feature values,using either supervised techniques (with learning samples)or unsupervised ones.In our application, features are usually gray level values and segmentation is very of-ten performed using two standard unsupervised techniques which are Gaussian Mixture Model?tting(GMM)and clustering.During the segmentation process,methods use geometrical assumptions regarding the location of the ventricle,in order to compensate the lack of spatial information,inherent to classi?cation based methods,but do not require user interaction in general.As for im-age-based methods,both contours may be processed differently,as shown below.

The principle of GMM is to?t the image histogram with a mix-ture of gaussians using the Expectation-Minimization(EM)algo-rithm(Dempster et al.,1977).The number of gaussians,that corresponds to the number of modes in the histogram,must be ?xed a https://www.sodocs.net/doc/bc4080956.html,ually,between two and?ve modes corresponding to encountered tissue types(myocardium,fat,background,blood pool for example)are chosen.Partial volume effect may be ac-counted for by adding gaussians representing partial voluming be-tween myocardium and blood,myocardium and air(Pednekar et al.,2006).Papillary muscles can be taken into account by assign-ing a mixture of two gaussians to the ventricle cavity:the blood pool and the myocardium(Sénégas et al.,2004).The EM algorithm can be initialized by using the preliminary step of heart localiza-tion(Pednekar et al.,2006)or using an atlas(Lorenzo-Valdés et al.,2004,see Section4.3.3).The EM segmentation result can be embedded in a cost matrix for dynamic programming(Pednekar et al.,2006),or precedes a step based on Markov Random Fields (MRF)in order to incorporate spatial correlations into the segmen-tation process(Gering,2003).The clustering approach consists in aggregating data in clusters in a feature space.Clustering can be performed through K-means algorithm(Lynch et al.,2006a)or fuz-zy C-means,a generalization of K-means allowing partial member-ship in classes(Boudraa,1997).After obtaining separate cluster regions,the LV cavity is identi?ed by computing the distance to a circle(Lynch et al.,2006a).The closest blood pool being the one of the RV,the wall between these two cavities is measured to assess the myocardium thickness,which acts as a guide for seg-menting the epicardium,using edge information.The epicardium contour is closed using a spline.

Only a few supervised approaches have been proposed,since they require a tedious learning phase,that consists in providing the algorithm with gray levels of labeled pixels.The learning can be performed manually,by clicking on a few sample pixels belong-ing to myocardium,blood and lung(Stalidis et al.,2002).Theses samples are provided to a generating-shrinking neural network, combined with a spatiotemporal parametric modeling.In this work,the epicardial boundary is found through a radial search, using the previously de?ned endocardial model.The learning phase may also be automatic,by designing a spatial mask that is applied onto the image,thus providing tissue samples.A k-nearest neighbor algorithm is then used to classify the image pixels,which yields a cost map for graphcut segmentation(Kedenburg et al., 2006).

4.2.3.Deformable models

Deformable models have been made popular through the sem-inal work by Kass et al.(1988),who introduced active contours,or snakes.Thanks to their?exibility,active contours have been widely used in medical image segmentation(Xu et al.,2000).There are iteratively deforming curves according to the minimization of an energy functional,comprising a data-driven term,that provides information about object frontiers and a regularization term,that controls the smoothness of the curve.This energy functional is minimized by implementing the Euler–Lagrange equations in a partial differential equation(PDE).In problems of curve evolution, the level set framework has been widely used because it allows for topological changes(Osher and Sethian,1988).It consists in considering the deforming contour as the zero level of a higher-dimensional function.This implicit representation of the contour allows in particular the segmentation of multiple objects(Caselles et al.,1993).Deformable models have widely been applied to the ventricle segmentation problem either in the parametric framework or in the implicit one using level sets(Yezzi et al., 1997;Battani et al.,2003).

In most of the studies,the regularization term does not change much and is often curvature-based.The contributions regarding ventricle segmentation using deformable models have mainly dealt with the design of the data-driven term.Initially gradient-based(Gupta et al.,1993;Ranganath,1995;Geiger et al.,1995) and thus sensitive to noise,region-based terms have been introduced,that are based on a measure of region homogeneity (Paragios,2002;Pluempitiwiriyawej et al.,2005;Chakraborty et al.,1996).In(Ben Ayed et al.,2009),the authors take into account the intensity distribution overlap that exists between myocardium and cavity,and background and myocardium,and introduce a new term in the functional that measures how close the overlaps are to a segmentation model,manually obtained in the?rst frame.The Gradient Vector Flow(GVF)has also been widely used(Xu and Prince,1998).Initially designed to pull the contour into the object concavities,the GVF consists in diffusing the gradient of an edge map.Authors have noticed that it increases ef?ciency regarding initialization and convergence,hence its wide use(Pham et al.,2001;Santarelli et al.,2003;Wang and Jia,2006; El Berbari et al.,2007).Other energy terms have been introduced: in particular,Paragios proposes a coupled propagation of the epi-cardial and endocardial contours that combines both GVF and level sets(Paragios,2002)(Fig.7).The relative positions of the endocar-

C.Petitjean,J.-N.Dacher/Medical Image Analysis15(2011)169–184175

dium and the epicardium are constrained inside the process.In or-der to ensure the smoothness of the contour,a parametric shape model(circle,elliptic,spline),e.g.the direct Fourier parameteriza-tion of the contour(Staib and Duncan,1992),allows for compact representation and facilitates the formulation of energy(Chakr-aborty et al.,1996;Gotardo et al.,2006).Note that contour initial-ization,a crucial step with deformable models,can be carried out through image preprocessing,such as MM operations(El Berbari et al.,2007)or the EM algorithm(Jolly,2006),or else user interac-tion,as shown in Table2.

Deformable models offer a great framework for3D extension (Pham et al.,2001;Zhukov et al.,2002;Montagnat and Delingette, 2005;Heiberg et al.,2005)2,thanks to3D mesh models,but also to obtain ventricle contours throughout the entire cardiac cycle.A?rst approach to temporal extension is to apply the segmentation result obtained on the image at time t on the following phase image at time t+1.Whereas it has the advantage of low complexity,this sequen-tial approach does not integrate motion information and does not really exploit the temporal aspect of heart motion.Recent ap-proaches have been developped,that apply constraints on temporal evolution of the contour or surface points:it can be a weak con-straint such as averaging point trajectories or a strong constraint such as encoding prior knowledge about cardiac temporal evolution (Montagnat and Delingette,2005;Lynch et al.,2008)or use a seg-mentation/registration coupled approach(Paragios et al.,2002).

Volumetric modeling is particularly useful for tracking the LV cavity over time,using a biomechnical model to constrain the seg-mentation(Pham et al.,2001;Papademetris et al.,2002;Sermesant et al.,2006;Yan et al.,2007;Billet et al.,2009;Casta et al.,2009). The LV myocardium is modeled as a linear elastic material de?ned by several parameters(Poisson’s ratio and Young’s modulus,or equivalently Lamé’s constants),integrated into the so-called stiff-ness matrix,that de?nes the material properties of the deforming body in three dimensions.(Pham et al.,2001;Papademetris et al., 2002).The contractility of the myocardium is linked to the muscle ?ber directions,which is not isotropic.Fiber orientation can be taken into account by considering different stiffness values for the different material axes(Papademetris et al.,2002).In(Billet et al.,2009),the?ber orientation is part of an electromechanical model,that simulates the cardiac electrophysiology.This model is used to regularize the deformations of the volumetric model. This volumetric meshing is usually initialized with2D contours manually obtained on the?rst phases.The mesh is then deformed using an image force and an internal force derived from the biome-chanical model as the regularizing term.Segmentation over the whole cardiac cycle is then obtained by minimizing an energy that couples the volumetric model deformations and the image data (Fig.8).The dynamic laws of motion expressed in PDE can be solved using?nite element method(Pham et al.,2001; Papademetris et al.,2002;Sermesant et al.,2003).Note that these approaches generally include further investigations regarding car-diac motion estimation,tissue deformation analysis and strain computation from3D image sequences.In particular,the introduc-tion of functional information regarding the heart can help recover tangential motion of the ventricle,which cannot be obtained from standard tracking approaches because of the aperture problem (Billet et al.,2009).

anatomical constraints for the segmentation of the left ventricle.Black contours:initial conditions

Science+Business Media).

volumetric mesh and associated segmentation result.(a)Mid-diastole image.(b)Segmented mesh with synthetic?ber directions.

the cardiac cycle(from(Billet et al.,2008),licensed under the Creative Commons3.0Unported License).

2The authors have made their software freely available for research purposes at

http://segment.heiberg.se/(Heiberg et al.,2010).

4.2.4.Conclusion

A wide variety of image-driven approaches using weak or no prior have been proposed to tackle the ventricle segmentation in cardiac MRI.Almost all of these methods require either minimal or great user intervention.If image-based and pixel classi?ca-tion-based approaches offer a limited framework for incorporating strong prior,extensions of deformable models in this sense have been extensively studied.In the next section are presented meth-ods relying on strong prior for heart segmentation.4.3.Segmentation with strong prior

As shown by the growing literature on this matter,automatic organ segmentation can bene?t from the use of a statistical model regarding shape and/or gray levels,to increase its robustness and accuracy.This especially applies if the nature of the shape does not change much from an individual to another,which is typically the case for the heart.Statistical-model based segmentation tech-niques comprise three steps:

1.Spatial (and temporal if required)alignment of manually seg-mented contours or images is of utmost importance to compen-sate for differences in ventricle position and size,and can be very dif?cult,especially in 3D (Frangi et al.,2002).One subject in the database is arbitrarily chosen as reference.Every other subject is af?nely registered on the reference,and an average shape is com-puted.This procedure can be performed iteratively by replacing the reference subject with the mean model.When segmentation is computed over the complete cardiac cycle,temporal aligne-ment is needed as well:the number of phases of each individual must be aligned on the number of phases of the reference and thus requires resampling and interpolation (Fig.9).

2.Model construction generally implies the computation of an average shape or image and the modeling of variability present in the training images and contours.This latter can be made via the widely used principal component analysis (PCA),when point correspondences between contour points are available,or else via variance computation or probability density model-ing.The PCA provides the eigenvalue decomposition of the shape set covariance matrix,yielding principal eigenvectors (or components),that account for as much of the variability in the data as possible.Each instance of a new shape can thus be described by the mean shape and a weighted linear combina-tion of eigenvectors.

3.The use of the model for segmentation speci?es how the aver-age model is applied to ?t the contours in a new image,while taking into account the variability present in the training set.Methods based on a statistical model mainly fall into three cat-egories:shape prior segmentation (Cremers et al.,2007),active

shape and appearance models (Heimann and Meinzer,2009)and atlas-based segmentation techniques (Rohl?ng et al.,2005).Whereas steps 1and 2are globally common to these methods,they mostly differ by step 3,i.e.the application of the model for segmentation.

4.3.1.Deformable models based segmentation using strong prior

The principle of deformable models with strong prior takes up the variational framework de?ned in Section 4.2.3.The principle is to modify the energy functional to be minimized by introducing a new term,that embeds an anatomical constraint on the deform-ing contour,such as a distance to a reference shape model.To con-struct the reference model,an original variational technique to compute the mean signed distance map is proposed in (Paragios et al.,2002).Then an aligment transformation to this reference is incorporated into the criterion to be minimized.A probability den-sity function (PDF)or probabilistic map can be computed from the data by adding the binary segmented images of both contours,after scaling and alignment.This PDF is embedded as a multiplica-tive term in the evolution equation (Lynch et al.,2006b )or inte-grated into the energy function of a graphcut (Lin et al.,2005).An alternative to the use of a PDE is proposed in (Tsai et al.,2003;Kaus et al.,2004),with the advantage of being fast and di-rect,based on a preliminary PCA on the training data.For a new contour to be segmented,eigenvector weights as well as pose parameters are iteratively updated with a gradient descent,by minimizing the region-based energy terms.Note that these ap-proaches include the coupled propagation of both contours,as al-ready seen in Section 4.2.3,that maintains the relative positions of the endocardium and the epicardium according to a distance model (Kaus et al.,2004;Lynch et al.,2006b;Paragios et al.,2002).To address the temporal aspect of segmentation,approaches relying on bayesian formulation are proposed (Sénégas et al.,2004;Sun et al.,2005).Under the statistical modeling of the image,the segmentation process becomes a MAP (Maximum A Posteriori)estimation.Here,the regularity term is based on the prior,that contains a shape model and a motion model,thus allowing to track ventricle contour over time.The shape model is obtained from an PCA (Sun et al.,2005)or based on a Fourier representation,whose parameters are learned on a database of segmented images (Sénégas et al.,2004).Segmentation is performed with Monte-Carlo techniques or particle ?ltering.

4.3.2.Active shape and appearance models

The ASM consist of a statistical shape model,called Point Distri-bution Model (PDM),obtained by a PCA on the set of aligned shapes,and a method for searching the model in an image (Cootes et al.,1995).Segmentation is performed by placing the model on the image,and iteratively estimating rotation,translation and scal-ing parameters using least square estimation,while

constraining

resampling:linear interpolation is used to generate time frames from image sequence B which correspond kind permission from Elsevier).

the weights of the instance shape to stay within suitable limits for similar shapes.ASM have been extended to gray level modeling,yielding Active appearance models (AAM)(Cootes et al.,1998),that represent both the shape and texture variability seen in a training set.This technique ensures to have a realistic solution,since only shapes similar to the training set are allowed.

AAM applied to LV segmentation are ?rst presented indepen-dently in (Mitchell et al.,2000)and in (Stegmann and Nilsson,2001),demonstrating the clinical potential of the approach for the segmentation of both the endocardium and epicardium.Strengths of AAM and ASM can be combined in a hybrid model (Mitchell et al.,2001;Zambal et al.,2006;Zhang et al.,2010).In (Mitchell et al.,2001),the authors introduce a multistage hybrid model,arguing that AAM are optimized on global appearance but provide imprecise border locations,whereas ASM have a great abil-ity to ?nd local structures.They thus propose to concatenate sev-eral independant matching phases,starting with an AAM early stage,that positions the model onto the heart,followed by a hybrid ASM/AAM stage,that allows for position re?nement.A ?nal stage of AAM aids in escaping a possible local minimum found during the ASM/AAM stage.Although this method provides accurate re-sults,and,for the ?rst time with AAM,results on the RV,the current model training has been limited to mid-ventricular,end-diastolic images.3The idea of combining AAM and ASM has also been used in (Zambal et al.,2006),where the global model con-struction consists in interconnecting a set of 2D AAM by a 3D shape model.The goal of the AAM is to match contours on each image individually,while the 3D shape model provides an overall consis-tency to the instance of the model.The obtained segmentation re-sult is improved on apical slices:if the local matching of 2D AAM fails,is it corrected by the 3D model,through iterative global and local matching.

Several modi?cations of the original framework have been pro-posed to increase segmentation accuracy such as the use of an Independent Component Analysis instead of a PCA (üzümcüet al.,2003),the introduction of ASM with Invariant Optimal Fea-tures (IOF)(Ordas et al.,2003),a technique that replaces the Maha-lanobis distance with feature selection from a set of optimal local features.The current model training for these studies has also been limited to mid-ventricular,end-diastolic images.The local search of corresponding points during ASM segmentation can be made

more reliable by the use of a robust estimator,such as Robust Point Matching,a technique that allows to match two sets of features using thin-plate splines (Abi-Nahed et al.,2006).This method has been speci?cally applied to the RV segmentation.

Extension of the model to the temporal dimension is proposed in (Lelieveldt et al.,2001),with the introduction of 2D+time Active Appearance Motion Model (AAMM).Authors propose to extend the 2D AAM framework by considering the whole sequence of images over one cardiac cycle and by modeling not only shape and gray levels of the heart,but also its motion.Because of the variation of the image number per cardiac cycle from one patient to another (from 16to 25in their study),a temporal normalization is re-quired,here set to 16phases.The model allows to obtain in a fast manner segmentation over the whole cardiac cycle,still limited to mid-ventricular slices.The extension to 3D AAM and ASM is pre-sented in (Mitchell et al.,2002;Stegmann and Pedersen,2005;van Assen et al.,2006),although not straightforward,especially be-cause the model requires point correspondences between shapes (a result is provided in Fig.10).The great amount of data to be pro-cessed in 3D models leads to an increase of the computational load,that can be lowered thanks to the use of techniques such as grid computing (Ordas et al.,2005).

4.3.3.Atlas-guided segmentation and registration

An atlas describes the different structures present in a given type of image.It can be generated by manually segmenting an im-age or by integrating information from multiple segmented images from different individuals.Given an atlas,an image can be seg-mented by mapping its coordinate space to that of the atlas,using a registration process (Rohl?ng et al.,2005).Widely applied to brain segmentation (Collins et al.,1995),this technique has also been used for heart segmentation.As shown in Fig.11,the princi-ple is to register the labeled atlas onto the image to be segmented,and then apply the obtained transformation to the atlas,so as to obtain the ?nal segmentation.Segmentation can thus easily be propagated throughout the cardiac cycle using the same principle.In the literature,the construction of an anatomical heart atlas is based either on a single segmented image (Lorenzo-Valdés et al.,2002),an average segmentation result obtained over a population of healthy volunteers (14Lorenzo-Valdés et al.,2004or 25L?tj?-nen et al.,2004),or a cadaver atlas (Zhuang et al.,2008).The atlas or model can be matched on a new individual using non-rigid reg-istration (NRR),a transformation that accounts for elastic deforma-tions.NRR consists in maximizing a similarity measure between a source image S (the atlas)and a target or reference image R

(the

3D AAM.(a)Manually identi?ed contours.(b)3D AAM determined segmentation of the left ventricle (from 3

In this work the segmentation is performed in full-size image volumes.The location of the approximate center of the LV cavity is found with a Hough transform and is used to place the AAM model on the target image.

unsegmented image).Since the atlas and the MR image can have non corresponding gray levels,the similarity criterion must only account for statistical dependencies between them.The most widely used criterion for NRR is the normalized mutual informa-tion measure E NMI(Studholme et al.,1999).Based on individual and joint gray level distributions,E NMI is de?ned as:

E NMIeS;RT?HeSTtHeRTHeS;RT

where H(á)denotes marginal entropy and H(á,á)joint entropy of cor-responding pixel or voxel pairs.Maximizing only the similarity cri-terion provides under-constrained equations,that makes image registration an ill-posed problem and thus requires the use of addi-tional constraints.One way is to restrict the transformation space to parametric transformations,such as cubic splines(Lorenzo-Valdés et al.,2002)or the basis of eigenshapes,obtained with a PCA on the database of shapes(L?tj?nen et al.,2004).Another way is to add a regularization term to the similarity criterion,such as a clas-sical viscous?uid model(Zhuang et al.,2008),or a statistical model (L?tj?nen et al.,2004).In this latter work,the variability of the shape in the database of subjects is used as a regularizer.This var-iability is modeled with probabilistic shape models,including a probabilistic atlas,that provides the probability that a structure ap-pears at each pixel.It is constructed by af?nely registering all sub-jects to the reference,blurring the registered segmentation image with a Gaussian kernel and averaging all blurred segmentation.It has the advantage not to require point correspondence.Note also the use of a probabilistic atlas in(Lorenzo-Valdés et al.,2004),to initialize the parameters of an EM algorithm.The segmentation ob-tained after convergence of the EM algorithm is re?ned thanks to contextual spatial information modeled through MRF.

4.3.4.Conclusion

By imposing constraints on the?nal contour through the use of a statistical model,strong prior based methods can overcome the previously de?ned segmentation problems(ill-de?ned cavity bor-ders,presence of papillary muscles and gray level variations around the myocardium),without the need of user interaction, but at the expense of manually building a training set.The compo-sition of the training set is questionable,as variability depend upon initial data.ASM cannot approximate data that are not in the train-ing set and have to be representative enough of all possible heart shapes to achieve high accuracy.In(Zhang et al.,2010),the authors compensate the limited size of their training set by introducing an-other source of information,namely a manual segmentation of the ?rst frame.The method is proved to be more robust but loses the bene?t of being user-independant.Note that incorporation of time dimension and the third dimension into the model is not straight-forward.For AAM,its high dimension increases the risk of over?t-ting.The non-rigid registration framework is more?exible,allowing for shapes not present in the training set,but does not im-pose anatomical constraint on the transformation.As a result,the composition of the atlas has little in?uence on the segmentation results,since it is only used as a starting point for registration (Zhuang et al.,2008).

5.Results and discussion

5.1.Methodological issues

Now let us examine how issues presented in Section3.2have been dealt with,according to the type of methods,by commenting Table2.Among the70papers that have been reviewed in this pa-per,only three studies are entirely devoted to the RV,while a quar-ter of the rest of the papers show segmentation results on both ventricles.As mentioned above,physical characteristics of the RV as well as its lesser vital function has restrained research efforts on its segmentation.Nonetheless there is a growing interest for the segmentation and volume computation of the RV,since MRI is increasingly used as a standard tool in the evaluation of RV func-tion,being the most accurate tool for assessing RV volume(Haddad et al.,2008).Furthermore,in some pathologies(RV out?ow tract obstruction,pulmonary stenosis,transposition of great vessels, tetralogy of Fallot,pulmonary hypertension),the RV is generally hypertrophied,which could help the segmentation process.Be-cause of the RV’s greatly varying shape,strong prior-based meth-ods and in particular,atlas-based methods,thanks to their ?exibility,seem of particular interest for RV segmentation.Regard-ing the endocardium and epicardium,they are of different nature, and thus present different segmentation challenges,as described in Section2.2.For some categories of methods,namely image-driven methods and pixel-classi?cation based approaches,speci?c solu-tions for each contour have been developed.On the contrary,mod-el-based approaches do not require the design of two different algorithms.One of the segmentation challenges is the handling of papillary muscles.Currently,the common segmentation stan-dards recommend to consider them as part as the ventricle cavity and to contour only the walls,because manual segmentation is more reproductible in this case,than when papillary muscles are segmented as well.But the exact cavity volume computation should exclude the volume occupied by the papillary muscles.In-deed,in some methods,pillars are segmented as well,authors arguing that the radiologist should decide whether to incorporate them or not(Pednekar et al.,2006;Lynch et al.,2006a).

Regarding the use of external information,this review high-lights the fact that knowledge is necessarily integrated into the segmentation process,be it during the initialization phase (through user interaction)or during the process itself,with differ-ent prior levels.The tradeoff between user interaction levels and levels of external information is illustrated in Fig.12.

5.2.Tracking the ventricle borders and motion information

Tracking the ventricle boundaries over time allows for full cycle segmentation and for further investigation of the cardiac deforma-tions and strain.Only a few methods really exploit the information provided by cardiac motion,partly because of the complexity and the variability of the motion model,but also because ED and ES im-age segmentations are suf?cient for estimating the cardiac con-tractile function in a clinical context.Making use of the temporal dimension can help the segmentation process and yields tempo-rally consistent solutions.Temporal continuity is also of interest for the clinician during manual segmentation,as he often examines images that follow or precede the image to be segmented in the se-quence,as well as corresponding images in slices below or above.

11.Anatomical atlas-based segmentation principle:(i)computation of

transformation T between the atlas and the image and(ii)deformation of the atlas

T(from(Lorenzo-Valdés et al.,2002),with kind permission from Springer

Science+Business Media).

Image Analysis15(2011)169–184179

The expert behavior can thus be emulated by considering that the neighborhood of each voxel includes its six spatial nearest neighbors and the voxels in the neighboring time frames of the sequence,such as in MRF (Lorenzo-Valdés et al.,2004)or in (Cousty et al.,2010),where a 4D graph corresponding to the 3D+t sequence is considered.

Tracking the ventricle contours over time may be performed with or without external knowledge.In this latter case,tracking re-lies on intrinsic properties of the segmentation methods.The var-iational approach of deformable models has been shown to be a very powerful and versatile framework for tracking the ventricle borders,the temporal resolution of cardiac MR allowing for using the segmentation result of the previous frame and propagating it to the next one (Pham et al.,2001;Gotardo et al.,2006;Hautvast et al.,2006;Ben Ayed et al.,2009).In order to improve the robust-ness of the propagation results,solutions have been proposed,like tracking the contours both backwards and forwards in time (Gotardo et al.,2006)and constraining the contour to respect the user’s preference by maintaining it to a constant position,through the matching of gray level pro?les (Hautvast et al.,2006).Non-rigid registration may also be used to propagate manually initialized contours or a heart atlas (Lorenzo-Valdés et al.,2002;Noble et al.,2003;Zhuang et al.,2008).In this case,the segmenta-tion boils down to a registration problem:the matching of one (segmented)image to the other (unsegmented)is then applied to the contour of the segmented image,thus providing the new de-formed contour.Segmentation and registration can also be coupled together by jointly searching for the epicardial and encocardial contours,and for an aligment transformation to a shape reference (Paragios et al.,2002).

Deformable models also are ef?cient for introducing knowledge regarding heart motion.Prior knowledge about cardiac motion can be encoded in a weak manner,by temporally averaging the trajec-tories of the contour or surface points (Heiberg et al.,2005;Montagnat and Delingette,2005),or in a strong manner,by using external prior.In (Lynch et al.,2008),the authors observed that the change of the blood volume of the ventricle is intrinsically linked to the boundary motion and that the volume decreases and increases during one cardiac cycle.The movement of the con-tour points are then modeled by an inverted Gaussian function,used to constrain the deformation of the level set.The dynamics of the heart can be taken into account via a biomechanical model (see Section 4.2.3),used not to predict motion but to regularize the deformations of the volumetric model.Another possibility is to consider the moving boundaries as constituting a dynamic sys-tem,whose state must be estimated thanks to observations (the

images)and a model,learned from training date (Sénégas et al.,2004;Sun et al.,2005).This explicit combination of shape and mo-tion information,together with the appearance of the heart,can also be embedded in an ASM/AAM framework as a single shape and intensity sample (Lelieveldt et al.,2001).

Tracking the LV and RV borders over time is feasible,mainly in a deformable model framework.Thanks to full cycle image segmen-tation,the volume variation throughout the cardiac cycle can be assessed,along with other quantitative parameters (such as strain),that have proven useful for assessing the cardiac contractile func-tion (Papademetris et al.,2002).

5.3.Assessment of segmentation accuracy

The assessment of segmentation accuracy greatly varies among reported works and ranges from visual qualitative assessment to perpendicular distance between contours computation,via surface overlap,ventricle volume,mass and EF computation.Note that al-most all of the strong prior-based approaches are assessed via the mean P2C error.Test conditions change also a lot from one study to another:the number of test images,the number and nature of the patients (healthy and/or pathological),the phase of the cardiac cy-cle (only ED vs.all phases),the slice levels (only mid-ventricular le-vel vs.all slice levels),not to forget that these conditions are sometimes not or only partly speci?ed.Since these conditions have an in?uence on the segmentation complexity and thus on the ?nal result,they have been reported in the result table (Table 3).The last lines of this table include the participants to the MICCAI’09challenge.For this contest,two databases 4(one for training and the other for validation)have been provided to the participants,and results are provided either on one or both databases.Interest-ingly,the methods that have been assessed during this challenge are quite representative of existing methods.They range from simple image-based techniques,as proposed in (Huang et al.,2009;Lu et al.,2009),to ASM and AAM (O’Brien et al.,2009;Wijnhout et al.,2009),including deformable models (Constantinides et al.,2009)(an im-proved version of El Berbari et al.,2007;Casta et al.,2009),taking over works from Pham et al.(2001),a 4D watershed framework (Marak et al.,2009),based on (Cousty et al.,2007),and Jolly’s im-age-based method (Jolly,2009)based on (Jolly et al.,2009).

In order to limit the scope of our comparison,and also because we search for the best estimate of segmentation accuracy,the

fo-vs.levels of prior in cardiac MR segmentation methods in terms of paper numbers.Each circle is proportional interaction.U2:Advanced user interaction.

4

The database of the MICCAI’09contest is the Sunnybrook Cardiac MR Database,available at https://www.sodocs.net/doc/bc4080956.html,/projects/cardiac-mr/?les/.

cus is given in this review to the mean distance between contours. The choice of mean P2C error allows for comparison with intra and inter-observer variability of manual contouring,which is in the range1–2mm(Ordas et al.,2005;van Assen et al.,2006;L?tj?nen et al.,2004;Marak et al.,2009).On the whole,reported errors com-pare favorably to this value.As expected,error is higher on the epi-cardial wall than on the endocardial one,an observation that can be made in19out of28results.Results obtained on ED images are also better than results obtained on other phases.Note that a few segmentation results,especially on the RV endocardium,have been obtained on a reduced number of slices,highlighting the dif-?culty of segmenting apical and basal images.Indeed,the segmen-tation error computed on the RV endocardium is higher than on the LV.In a restricted number of papers,the spatial distribution of errors is reported(Lin et al.,2006;Jolly et al.,2009;Grosgeorge et al.,in press).These studies con?rm that segmentation is more dif?cult in apical than in basal or mid-ventricular slices.Because testing conditions differ from one study to another,it seems dif?-cult to draw more conclusions.Regarding the MICCAI Challenge studies,the ambiguity in the database choice does not allow for full comparison of the results.Nonetheless,one can note that among the eight involved methods,the ones obtaining the best results are image-based techniques(Lu et al.,2009;Huang et al.,2009). However these techniques require user interaction and cannot as-sess the ventricular surface in all phases,contrary to O’Brien et al. (2009),Casta et al.(2009),and Jolly(2009).In this sense,the work described in(Jolly,2009)offers a good compromise.Although results presented in(Lu et al.,2009;Huang et al.,2009;Constantinides et al.,2009)are interesting,their associated methodologies are LV speci?c and not applicable to the RV,whereas it would directly be possible with more generic methodologies such as in(O’Brien et al.,2009;Wijnhout et al.,2009).These trends illustrate the compromise between method performance and speci?city.

6.Conclusion and perspectives

This paper has been presenting segmentation methods in car-diac MRI.It seemed important to us to integrate the recent devel-opments of the last decade about prior knowledge based segmentation,almost10years after Frangi et al.’s comprehensive review(Frangi et al.,2001).We have proposed a categorization for these methods,highlighting the key role of the type of prior information used during segmentation,and have distinguished three levels of information:(i)no information is used,but our study shows that in this case user interaction is required,(ii)weak prior,that is,low level information such as anatomical assump-tions on the ventricle shape or biomechnical models,often com-bined to low-level user interaction,(iii)strong prior such as statistical models,constructed or learned from a large number of manually segmented images,usually not requiring user interac-tion.Our image segmentation categorization includes on the one hand image-driven and pixel classi?cation based approaches,and deformable models,making use of weak or no prior.On the other hand,strong prior approaches comprise shape prior deformable models,ASM and AAM,and atlas-based segmentation.We have tried to point out that segmentation results on cardiac images are obtained in diverse experimental conditions,on different,pri-vate image databases and using varying error measures,thus mak-ing it dif?cult to conclude on the ef?ciency or the superiority of

Table3

Reported segmentation errors.When multiple results were available,only best results were reported.H/P:number of healthy(H)and pathological(P)subjects.

Authors Nb H/P Slice Phases Mean errors(mm)

Subj.nb.LV epi LV endo RV endo

Mitchell et al.(2001)2011/93mid ED 1.75±0.83b 1.71±0.82b 2.46±1.39b Lelieveldt et al.(2001)25c–3mid all0.77±0.740.63±0.65–Mitchell et al.(2002)56c38/188–14ED 2.63±0.76 2.75±0.86–Ordas et al.(2003)7413/613mid5ph. 1.52±2.01d 1.80±1.74 1.20±1.47 Kaus et al.(2004)1210/1217–10ED 2.62±0.75 2.28±0.93–

ES 2.92±1.38 2.76±1.02–Sénégas et al.(2004)30–5ED 1.98a 1.34a–

ES 2.74a 2.62a–üzümcüet al.(2006)202/188–12All 1.77±0.57 1.86±0.59–Lynch et al.(2006a)25–5–12ED,ES 1.31±1.860.69±0.88–Lynch et al.(2006b)4––All 1.83±1.850.76±1.09–van Assen et al.(2006)1515/010–12ED 2.23±0.46 1.97±0.54–Abi-Nahed et al.(2006)13––ED–– 1.1a Lorenzo-Valdés et al.(2004)100/103mid All 2.99±2.65 2.21±2.22 2.89±2.56

ED 2.75±2.62 1.88±2.00 2.26±2.13 L?tj?nen et al.(2004)25c25/04–5ED 2.77±0.49d 2.01±0.31 2.37±0.5 Hautvast et al.(2006)69–9/14ES 1.84±1.04 2.23±1.10 2.02±1.21 El Berbari et al.(2007)13–3ED 1.3±0.70.6±0.3–Jolly et al.(2009)19–All ED,ES 2.91a,b 2.48a,b–Zhang et al.(2010)2525/0–All 1.67±0.3 1.81±0.4 2.13±0.39

250/25–All 1.71±0.45 1.97±0.58 2.92±0.73 Cousty et al.(2010)180/189–14ED,ES 1.42±0.36 1.55±0.23–Casta et al.(2009)e153/126–12ED,ES 2.72a––Wijnhout et al.(2009)e153/126–12ED,ES 2.29a 2.28a–Lu et al.(2009)e153/126–12ED,ES 2.07±0.61 1.91±0.63–Marak et al.(2009)e––6–12ED,ES3±0.59 2.6±0.38–Constantinides et al.(2009)e306/246–12ED,ES 2.04±0.47 2.35±0.57–Jolly(2009)e306/246–12ED,ES 2.26±0.59 1.97±0.48–Huang et al.(2009)e306/246–12ED,ES 2.06±0.39 2.11±0.41–O’Brien et al.(2009)e306/246–12ED,ES 3.73a 3.16a–

a No standard deviation provided.

b RMS error.

c Leave-one-out protocol.

d RV epicardium is taken into account for error computation.

e Study part o

f the MICCAI contest.

C.Petitjean,J.-N.Dacher/Medical Image Analysis15(2011)169–184181

one method over the others.One result of our review is that seg-mentation results are generally satisfying for the LV,especially on mid-ventricular slices,since the precision is of the order of manual tracing variability.These results are suitable for clinical pratice and?nally show that room for improvement on the LV seg-mentation problem might be limited and restricted to basal and apical slices.Just as image segmentation can rely on anatomical modeling,tracking the ventricle borders over a complete cardiac cycle can rely on prior knowledge regarding the heart motion(such as a biomechanic model).Tracking allows to compute the varia-tions of the ventricle volumes and other quantitative parameters. It is thus a complementary way to characterize the heart contrac-tile function,that may be used in clinical routine in the future. Regarding the RV segmentation results,one can note that they have been reported in a limited number of papers.This task is still a critical issue,due to the varying and complex shape of this ven-tricle and to its thin and ill-de?ned borders,appearing fuzzy in api-cal slices.

Other challenging image processing and pattern recognition is-sues connected to short axis MR images include the automatic identi?cation of ED and ES frames,currently made by browsing through all the image sequence(Lalande et al.,2004),and the determination of imaging slices that can be taken into account for volume computation.Physicians are confronted to a hard choice for those situated around the base of the heart,since certain slices are above the ventricle and include atria.

Until now,the problem of LV segmentation was still open, partly because of the lack of publicly available image database and of common performance evaluation protocols.Today,image databases,such as the one made available during the MICCAI’09 contest,exist and should be used to assess and compare further proposed algorithms,along with the proposed validation tools, namely the mean perpendicular distance between contours,and the Dice metric(surface overlap).An equivalent database of seg-mented images would be of great help for RV segmentation,espe-cially because of the growing interest for this very challenging segmentation task.

References

Abi-Nahed,J.,Jolly,M.-P.,Yang,G.-Z.,2006.Robust active shape models:a robust, generic and simple automatic segmentation tool.In:Proceedings of Medical Image Computing and Computer-Assisted Intervention(MICCAI),vol.2,pp.1–8. Allender,S.,Scarborough,P.,Peto,V.,Rayner,M.,Leal,J.,Luengo-Fernandez,R.,Gray,

A.,2008.European cardiovascular disease statistics.European Heart Network. Battani,R.,Corsi,C.,Sarti,A.,Lamberti,C.,Piva,T.,Fattori,R.,2003.Estimation of right ventricular volume without geometrical assumptions utilizing cardiac magnetic resonance https://www.sodocs.net/doc/bc4080956.html,puters in Cardiology30,81–84.

Ben Ayed,I.,Li,S.,Ross,I.,2009.Embedding overlap priors in variational left ventricle tracking.IEEE Trans.Med.Imag.28(12),1902–1913.

Billet,F.,Sermesant,M.,Delingette,H.,Ayache,N.,2008.Cardiac motion recovery by coupling an electromechanical model and cine-MRI data:?rst steps.In: Proceedings of the Workshop on Computational Biomechanics for Medicine III (Workshop MICCAI).

Billet,F.,Sermesant,M.,Delingette,H.,Ayache,N.,2009.Cardiac motion recovery and boundary conditions estimation by coupling an electromechanical model and cine-MRI data.In:Proceedings of Functional Imaging and Modeling of the Heart(FIMH),LNCS,vol.5528,pp.376–385.

Bland,J.,Altman,D.,1986.Statistical methods for assessing agreement between two methods of clinical https://www.sodocs.net/doc/bc4080956.html,ncet1(8476),307–310.

Boudraa, A.-E.-O.,1997.Automated detection of the left ventricular region in magnetic resonance images by fuzzy C-means model.Int J Card Imaging13(4), 347–355.

Caselles,V.,Catte,F.,Coll,T.,Dibos,F.,1993.A geometric model for active contours.

Numerische Mathematik66,1–31.

Cassen,C.,Domenger,J.,Braquelaire,J.,Barat,J.,2001.Left ventricular segmentation in MRI images.In:Proceedings of the2nd International Symposium on Image and Signal Processing and Analysis(ISPA),Pula,Croatia,pp.244–249.

Casta, C.,Clarysse,P.,Schaerer,J.,Pousin,J.,2009.Evaluation of the dynamic deformable elastic template model for the segmentation of the heart in MRI sequences.In:MICCAI2009Workshop on Cardiac MR Left Ventricle Segmentation Challenge,MIDAS Journal.Caudron,J.,Fares,J.,Bauer,F.,Dacher,J.-N.,2011.Left ventricular diastolic function assessment by cardiac MRI.RadioGraphics31(1).doi:10.1148/rg.311105049. Chakraborty,A.,Staib,L.,Duncan,J.,1996.Deformable boundary?nding in medical images by integrating gradient and region information.IEEE Trans.Med.Imag.

15,859–870.

Cocosco,C.,Netsch,T.,Senegas,J.,Bystrov,D.,Niessen,W.,Viergever,M.,2004.

Automatic cardiac region-of-interest computation in cine3D structural MRI.In: Proceedings of the Conference on Computer Assisted Radiology and Surgery (CARS),Chicago,pp.1126–1131.

Cocosco,C.,Niessen,W.,Netsch,T.,Vonken,E.-J.,Lund,G.,Stork,A.,Viergever,M., 2008.Automatic image-driven segmentation of the ventricles in cardiac cine MRI.J.Magn.Reson.Imag.28(2),366–374.

Collins, D.,Holmes, C.,Peters,T.,Evans, C.,1995.Automatic3-D model-based neuroanatomical segmentation.Human Brain Map.3(3),190–208. Constantinides, C.,Chenoune,Y.,Kachenoura,N.,Roullot, E.,Mousseaux, E., Herment,A.,Frouin,F.,2009.Semi-automated cardiac segmentation on cine magnetic resonance images using GVF-Snake deformable models.In:MICCAI 2009Workshop on Cardiac MR Left Ventricle Segmentation Challenge.MIDAS Journal.

Cootes,T.,Cooper,D.,Taylor,C.,Graham,J.,1995.Active shape models–their training and https://www.sodocs.net/doc/bc4080956.html,put.Vis.Image Understand.61(1),38–59. Cootes,T.,Edwards,G.,Taylor, C.,1998.Interpreting face images using active appearance models.In:Proceedings European Conference on Computer Vision, vol.2.pp.484–498.

Cousty,J.,Najman,L.,Couprie,M.,Clment-Guinaudeau,S.,Goissen,T.,Garot,J., 2007.Automated,accurate and fast segmentation of4D cardiac MR images.In: Proceedings of Functional Imaging and Modeling of the Heart(FIMH),LNCS,vol.

4466,pp.474–483.

Cousty,J.,Najman,L.,Couprie,M.,ClTment-Guinaudeau,S.,Goissen,T.,Garot,J., 2010.Segmentation of4D cardiac MRI:automated method based on spatio-temporal watershed cuts.Image https://www.sodocs.net/doc/bc4080956.html,put.28,1229–1243.

Cremers,D.,Rousson,M.,Deriche,R.,2007.A review of statistical approaches to level set segmentation:integrating color,texture,motion and shape.Int.J.

Comput.Vis.72(2),195–215.

Dawant,B.,Zijdenbos,A.,2000.Image segmentation.In:Sonka,M.,Fitzpatrick,J.

(Eds.),Handbook of Medical Imaging,vol.2.SPIE Press,Bellingham,WA,pp.71–127.

Dempster,A.,Laird,N.,Rubin,D.,1977.Maximum likelihood from incomplete data via the EM algorithm.J.Roy.Stat.Soc.39(1),1–38.

El Berbari,R.,Bloch,I.,Redheuil,A.,Angelini,E.,Mousseaux,E.,Frouin,F.,Herment,

A.,2007.An automated myocardial segmentation in cardiac MRI.Conf.Proc.

IEEE Eng.Med.Biol.Soc.1,4508–4511.

Fran?ois,C.,Fieno,D.,Shors,S.,Finn,P.,2004.Left ventricular mass:manual and automatic segmentation of true FISP and FLASH cine MR images in dogs and pigs.Radiology230,389–395.

Frangi,A.,Rueckert,D.,Schnabel,J.,Niessen,W.,2002.Automatic construction of multiple-object three-dimensional statistical shape models:application to cardiac modeling.IEEE Trans.Med.Imag.21(9),1151–1166.

Frangi, A.,Niessen,W.,Viergever,M.,2001.Three-dimensional modeling for functional analysis of cardiac images:a review.IEEE Trans.Med.Imag.20(1). Fu,J.,Chai,J.,Wong,S.,2000.Wavelet-based enhancement and detection of left ventricular endocardial and epicardial boundaries in magnetic resonance images.Magn.Reson.Imag.18,1135–1141.

Geiger, D.,Gupta, A.,Costa,L.,Vlontzos,J.,1995.Dynamic programming for detecting,tracking and matching deformable contours.IEEE Trans.Pattern Anal.

Machine Intell.19(6),294–302.

Gering,D.,2003.Automatic segmentation of cardiac MRI.In:Proceedings of Medical Image Computing and Computer-Assisted Intervention(MICCAI).LNCS,vol.1, pp.524–532.

Goshtasby,A.,Turner,D.,1995.Segmentation of cardiac cine MR images for extrac-tion of right and left ventricular chambers.IEEE Trans.Med.Imag.14(1),56–64. Gotardo,P.,Boyer,K.,Saltz,J.,Raman,S.,2006.A new deformable model for boundary tracking in cardiac MRI and its application to the detection of intra-ventricular dyssynchrony.In:IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR’06),vol.1,pp.736–743.

Grosgeorge,D.,Petitjean,C.,Caudron,J.,Fares,J.,Dacher,J.-N.,in press.Automatic cardiac ventricle segmentation in MR images:a validation https://www.sodocs.net/doc/bc4080956.html,put.

Assist.Radiol.Surg.doi:doi:10.1007/s11548-010-0532-6.

Gupta,A.,von Kurowski,L.,Singh,A.,Geiger,D.,Liang,C.-C.,Chiu,M.-Y.,Adler,L., Haacke,M.,Wilson,D.,1993.Cardiac MR image segmentation using deformable models.In:IEEE Conference on Computers in Cardiology,London,UK,pp.747–750.

Haddad,F.,Hunt,S.,Rosenthal,D.,Murphy,D.,2008.Right ventricular function in cardiovascular disease,part I–anatomy,physiology,aging,and functional assessment of the right ventricle.Circulation117,1436–1448.

Hautvast,G.,Lobregt,S.,Breeuwer,M.,Gerritsen, F.,2006.Automatic contour propagation in cine cardiac magnetic resonance images.IEEE Trans.Med.Imag.

25(11),1472–1482.

Heiberg,E.,Sj?gren,J.,Ugander,M.,Carlsson,M.,Engblom,H.,Arheden,H.,2010.

Design and validation of segment–a freely available software for cardiovascular image analysis.BMC Medical Imaging10,1.

Heiberg,E.,Wigstr?m,L.,Carlsson,M.,Bolger,A.,Karlsson,M.,2005.Time resolved three-dimensional segmentation of the left ventricle.In:Proceedings of IEEE Computers in Cardiology,vol.32,Lyon,France,pp.599–602.

Heimann,T.,Meinzer,H.-P.,2009.Statistical shape models for3D medical image segmentation:a review.Medical Image Analysis13(4),543–563.

182 C.Petitjean,J.-N.Dacher/Medical Image Analysis15(2011)169–184

Huang,J.,Huang,X.,Metaxas, D.,Axel,L.,2007.Dynamic texture based heart localization and segmentation in4-D cardiac images.In:Proceedings of the IEEE Intl Symposium on Biomedical Imaging:From Nano to Macro(ISBI),pp.852–855.

Huang,S.,Liu,J.,Lee,L.,Venkatesh,S.,Teo,L.,Au, C.,Nowinski,W.,2009.

Segmentation of the left ventricle from cine mr images using a comprehensive approach.In:MICCAI2009Workshop on Cardiac MR Left Ventricle Segmentation Challenge.MIDAS Journal.

Jolly,M.-P.,2006.Automatic segmentation of the left ventricle in cardiac MR and CT images.International Journal of Computer Vision70(2),151–163.

Jolly,M.-P.,2008.Automatic recovery of the left ventricular blood pool in cardiac cine MR images.In:Proceedings of Medical Image Computing and Computer-Assisted Intervention(MICCAI).LNCS5241,pp.110–118.

Jolly,M.-P.,2009.Fully Automatic Left Ventricle Segmentation in Cardiac Cine MR Images Using Registration and Minimum Surfaces.In:MICCAI2009Workshop on Cardiac MR Left Ventricle Segmentation Challenge.MIDAS Journal.

Jolly,M.-P.,Duta,N.,Funka-Lea,G.,2001.Segmentation of the left ventricle in cardiac MR images.Proceedings of the Eighth IEEE International Conference on Computer Vision,ICCV2001,vol.1.Vancouver,BC,Canada,pp.501–508. Jolly,M.-P.,Xue,H.,Grady,L.,J.,Gühring,https://www.sodocs.net/doc/bc4080956.html,bining registration and minimum surfaces for the segmentation of the left ventricle in cardiac cine MR images.In:Proceedings of Medical Image Computing and Computer-Assisted Intervention(MICCAI).LNCS5762,pp.910–918.

Kass,M.,Witkin, A.,Terzopoulos, D.,1988.Snakes:active contour models.

International Journal of Computer Vision1,321–332.

Katouzian,A.,Konofagou,E.,Prakash,A.,2006.A new automated technique for left-and right-ventricular segmentation in magnetic resonance imaging.Conf.Proc.

IEEE Eng.Med.Biol Soc.1,3074–3077.

Kaus,M.,von Berg,J.,Weese,J.,Niessen,W.,Pekar,V.,2004.Automated segmentation of the left ventricle in cardiac MRI.Med.Image Anal.8(3), 245–254.

Kaushikkar,S.,Li, D.,Haale, E.,Dávila-Román,V.,1996.Adaptive blood pool segmentation in three-dimensions:application to MR cardiac evaluation.J.

Magn.Reson.Imag.6,690–697.

Kedenburg,G.,Cocosco,C.,K?the,U.,Niessen,W.,Vonken,E.,Viergever,M.,2006.

Automatic cardiac MRI myocardium segmentation using graphcut.In: Proceedings of SPIE,Medical Imaging,vol.6144.

Lalande,A.,Legrand,L.,Walker,P.,Guy,F.,Cottin,Y.,Roy,S.,Brunotte,F.,1999.

Automatic detection of left ventricular contours from cardiac cine magnetic resonance imaging using fuzzy logic.Investig.Radiol.34(3),211–217. Lalande,A.,Salvé,N.,Comte,A.,Jaulent,M.-C.,Legrand,L.,Walker,P.,Cottin,Y., Wolf,J.,Brunotte,F.,2004.Left ventricular ejection fraction calculation from automatically selected and processed diastolic and systolic frames in short-axis cine-MRI.J.Cardiovasc.Magn.Reson.6(4),817–827.

Lee,H.,Codella,N.,Cham,M.,Prince,M.,Weinsaft,J.,Wang,Y.,2008.Left ventricle segmentation using graph searching on intensity and gradient and a priori knowledge(lvGIGA)for short-axis cardiac magnetic resonance imaging.J.

Magn.Reson.Imag.28(6),1393–1401.

Lelieveldt,B.,Mitchell,S.,Bosch,J.,van der Geest,R.,Sonka,M.,Reiber,J.,2001.

Time-continuous segmentation of cardiac image sequences using active appearance motion models.In:Information Processing in Medical Imaging: 17th International Conference(IPMI).LNCS,Davis,CA,USA,p.446.

Lin,X.,Cowan,B.,Young,A.,2005.Model-based graph cut method for segmentation of the left ventricle.In:IEEE International Conference of the Engineering in Medicine and Biology Society(EMBS),pp.3059–3062.

Lin,X.,Cowan,R.,Young,A.,2006.Automated detection of left ventricle in4D MR images:experience from a large study.In:Proceedings of Medical Image Computing and Computer-Assisted Intervention(MICCAI),LNCS,vol.9,pp.

728–735.

Liu,N.,Strugnell,W.,Slaughter,R.,Riley,R.,Crozier,S.,Wilson,S.,Liu,F.,Appleton,

B.,Trakic,A.,Wei,Q.,2005.Right ventricle extraction by low level and model-

based algorithm.In:Proceedings of the IEEE Engineering in Medicine and Biology Society(EMBS),pp.1607–1610.

Lloyd-Jones,D.,2010.Heart Disease and Stroke Statistics–2010Update.American Heart Association.

Lorenzo-Valdés,M.,Sanchez-Ortiz,G.,Elkington,A.,Mohiaddin,R.,Rueckert,D., 2004.Segmentation of4D cardiac MR images using a probabilistic atlas and the EM algorithm.Med.Image Anal.8(3),255–265.

Lorenzo-Valdés,M.,Sanchez-Ortiz,G.,Mohiaddin,R.,Rueckert,D.,2002.Atlas-based segmentation and tracking of3D cardiac MR images using non-rigid registration.In:Proceedings of Medical Image Computing and Computer-Assisted Intervention(MICCAI).LNCS,Tokyo,Japan,pp.642–650.

L?tj?nen,J.,Kivist?,S.,Koikkalainen,J.,Smutek,D.,Lauerma,K.,2004.Statistical shape model of atria,ventricles and epicardium from short-and long-axis MR images.Med.Image Anal.8(3),371–386.

Lu,Y.,Radau,P.,Connelly,K.,Dick,A.,Wright,G.,2009.Automatic image-driven segmentation of left ventricle in cardiac cine MRI.In:MICCAI2009Workshop on Cardiac MR Left Ventricle Segmentation Challenge.MIDAS Journal. Lynch,M.,Ghita,O.,Whelan,P.,2006a.Automatic segmentation of the left ventricle cavity and myocardium in MRI https://www.sodocs.net/doc/bc4080956.html,put.Biol.Med.36(4),389–407. Lynch,M.,Ghita,O.,Whelan,P.,2006b.Left-ventricle myocardium segmentation using a coupled level-set with a priori https://www.sodocs.net/doc/bc4080956.html,put.Med.Imag.Graph.

30(4),255–262.

Lynch,M.,Ghita,O.,Whelan,P.,2008.Segmentation of the left ventricle of the heart in3D+t MRI data using an optimised non-rigid temporal model.IEEE Trans.

Med.Imag.27(2),195–203.Mahnken,A.,Mühlenbruch,G.,Koos,R.,Stanzel,S.,Busch,P.,Niethammer,M., Günther,R.,Wildberger,J.,2006.Automated vs.manual assessment of left ventricular function in cardiac multidetector row computed tomography: comparison with magnetic resonance imaging.J.Eur.Radiol.16(7),1416–1423. Marak,L.,Cousty,J.,Najman,L.,Talbot,H.,2009.4D morphological segmentation and the MICCAI LV-segmentation grand challenge.In:MICCAI2009Workshop on Cardiac MR Left Ventricle Segmentation Challenge.MIDAS Journal. Mitchell,S.,Bosch,J.,Lelieveldt,B.,van der Geest,R.,Reiber,J.,Sonka,M.,2002.3-D active appearance models:segmentation of cardiac MR and ultrasound images.

IEEE Trans.Med.Imag.21(9),1167–1178.

Mitchell,S.,Lelieveldt,B.,van der Geest,R.,Bosch,J.,Reiber,J.,Sonka,M.,2000.

Segmentation of cardiac MR images:an active appearance model approach.In: Proceedings of SPIE,Medical Imaging,vol.3979,pp.224–234.

Mitchell,S.,Lelieveldt,B.,van der Geest,R.,Bosch,J.,Reiber,J.,Sonka,M.,2001.

Multistage hybrid active appearance model matching:segmentation of left and right ventricles in cardiac MR images.IEEE Trans.Med.Imag.20(5),415–423.

Montagnat,J.,Delingette,H.,2005.4D deformable models with temporal constraints:application to4D cardiac image segmentation.Med.Image Anal.

9(1),87–100.

Nachtomy,E.,Cooperstein,R.,Vaturi,M.,Bosak,E.,Vered,Z.,Akselrod,S.,1998.

Automatic assessment of cardiac function from short-axis MRI:procedure and clinical evaluation.Magn.Reson.Imag.16,365–376.

Noble,N.,Hill,D.,Breeuwer,M.,Schnabel,J.,Hawkes,D.,Gerritsen,F.,Razavi,R., 2003.Myocardial delineation via registration in a polar coordinate system.

Acad.Radiol.10(12),1349–1358.

O’Brien,S.,Ghita,O.,Whelan,P.,2009.Segmenting the left ventricle in3D using a coupled ASM and a learned non-rigid spatial model.In:MICCAI2009Workshop on Cardiac MR Left Ventricle Segmentation Challenge.MIDAS Journal.

O’Donnell,T.,Funka-Lea,G.,Tek,H.,Jolly,M.-P.,Rasch,M.,https://www.sodocs.net/doc/bc4080956.html,prehensive cardiovascular image analysis using MR and CT at Siemens Corporate Research.

https://www.sodocs.net/doc/bc4080956.html,put.Vis.70(2),165–178.

Ordas,S.,Boisrobert,L.,Huguet,M.,Frangi,A.,2003.Active shape models with invariant optimal features(IOF-ASM)–application to cardiac MRI https://www.sodocs.net/doc/bc4080956.html,put.Cardiol.30,633–636.

Ordas,S.,van Assen,H.,Boisrobert,L.,Laucelli,M.,Puente,J.,Lelieveldt,B.,Frangi,A., 2005.Statistical modeling and segmentation in cardiac MRI using a grid computing approach.In:Advances in Grid Computing–EGC2005.LNCS,pp.6–

15.

Osher,S.,Sethian,J.,1988.Fronts propagating with curvature-dependent speed: algorithms based on hamiltonjacobi https://www.sodocs.net/doc/bc4080956.html,put.Phys.79,12–49. Otsu,N.,1979.A threshold selection method for gray-level histograms.IEEE Trans.

Syst.Man.Cybernet.1,62–66.

Papademetris,X.,Sinusas,A.,Dione,D.,Constable,R.,Duncan,J.,2002.Estimating 3D left ventricular deformation from3D medical image sequences using biomechanical models.IEEE Trans.Med.Imag.21(7),786–800.

Paragios,N.,2002.A variational approach for the segmentation of the left ventricle in cardiac image https://www.sodocs.net/doc/bc4080956.html,put.Vis.50(3),345–362.

Paragios,N.,Rousson,M.,Ramesh,V.,2002.Knowledge-based registration and segmentation of the left ventricle:a level set approach.In:Proceedings of the 6th IEEE Workshop on Applications of Computer Vision(WACV),pp.37–42. Pavani,S.-K.,Delgado, D.,Frangi, A.,2010.Haar-like features with optimally weighted rectangles for rapid object detection.Patt.Recog.43(1),160–172. Pednekar, A.,Kurkure,U.,Muthupillai,R.,Flamm,S.,2006.Automated left ventricular segmentation in cardiac MRI.IEEE Trans.Biomed.Eng.53(7), 1425–1428.

Pham,D.,Xu,C.,Prince,J.,2000.Current methods in medical image segmentation.

Annu.Rev.Biomed.Eng.,Annu.Rev.2,315–337.

Pham,Q.,Vincent, F.,Clarysse,P.,Croisille,P.,Magnin,I.,2001.A FEM-based deformable model for the3D segmentation and tracking of the heart in cardiac MRI.In:Proceedings of the2nd International Symposium on Image and Signal Processing and Analysis(ISPA),Pula,Croatia,pp.250–254. Pluempitiwiriyawej,C.,Moura,J.,Wu,Y.,Ho,C.,2005.STACS:new active contour scheme for cardiac MR image segmentation.IEEE Trans.Med.Imag.24(5),593–603.

Ranganath,S.,1995.Contour extraction from cardiac MRI studies using snakes.IEEE Trans.Med.Imag.14(2),328–338.

Rohl?ng,T.,Brandt,R.,Menzel,R.,Russakoff,D.,Maurer,C.,2005.Quo vadis,atlas-based segmentation?In:The Handbook of Medical Image Analysis: Segmentation and Registration Models.Kluwer.Ch.11.

Rougon,N.,Petitjean, C.,Cluzel,P.,Prêteux, F.,Grenier,P.,2005.A non rigid registration approach for quantifying myocardial contraction in tagged MRI using generalized information measures.Med.Image Anal.9(4),353–375. Santarelli,M.,Positano,V.,Michelassi,C.,Lombardi,M.,Landini,L.,2003.Automated cardiac MR image segmentation:theory and measurement evaluation.Med.

Eng.Phys.25(2),149–159.

Sénégas,J.,Cocosco,C.,Netsch,T.,2004.Model-based segmentation of cardiac MRI cine sequences:a bayesian formulation.In:Fitzpatrick,J.,Sonka,M.(Eds.), Proceedings of SPIE,Medical Imaging,vol.5370,pp.432–443.

Sermesant,M.,Forest,C.,Pennec,X.,Delingette,H.,Ayache,N.,2003.Deformable biomechanical models:application to4D cardiac image analysis.Med.Image Anal.7(4),475–488.

Sermesant,M.,Moireau,P.,Camara,O.,Sainte-Marie,J.,Andriantsimiavona,R., Cimrman,R.,Hill,D.L.,Chapelle,D.,Razavi,R.,2006.Cardiac function estimation from mri using a heart model and data assimilation:advances and dif?culties.

Med.Image Anal.10(4),642–656.

C.Petitjean,J.-N.Dacher/Medical Image Analysis15(2011)169–184183

Shors,S.,Fung,C.,Fran?ois,C.,Finn,P.,Fieno,D.,2004.Accurate quanti?cation of right ventricular mass at MR imaging by using cine true fast imaging with steady-state precession:study in dogs.Radiology230(2),383–388.

Staib,L.,Duncan,J.,1992.Boundary?nding with parametrically deformable models.

IEEE Trans.Patt.Anal.Mach.Intell.14(11),1061–1075.

Stalidis,G.,Maglaveras,N.,Efstratiadis,S.,Dimitriadis,A.,Pappas,C.,2002.Model-based processing scheme for quantitative4-D cardiac MRI analysis.IEEE Trans.

Inf.Technol.Biomed.6(1),59–72.

Stegmann,M.,Nilsson,J.,Gr?nning,B.,2001.Automated segmentation of cardiac magnetic resonance images.Proc.Intl.Soc.Mag.Reson.Med.9,827. Stegmann,M.,Pedersen,D.,2005.Bi-temporal3D active appearance models with applications to unsupervised ejection fraction estimation.In:International Symposium on Medical Imaging2005,San Diego,CA,Proceedings of SPIE,vol.

5747,pp.336–350.

Studholme,C.,Hill,D.,Hawkes,D.,1999.An overlap invariant entropy measure of 3D medical image alignment.Patt.Recog.32(1),71–86.

Sun,W.,Cetin,M.,Chan,R.,Reddy,V.,Holmvang,G.,Chandar,V.,Willsky,A.,2005.

Segmenting and tracking the left ventricle by learning the dynamics in cardiac images.In:Proceedings of the Information Processing in Medical Imaging Conference(IPMI),pp.553–565.

Suri,J.,https://www.sodocs.net/doc/bc4080956.html,puter vision,pattern recognition and image processing in left ventricle segmentation:the last50years.Patt.Anal.Appl.3(3), 209–242.

Suri,J.,Singh,S.,Setarehdan,S.,Sharma,R.,Bovis,K.,Comaniciu,D.,Reden,L.,2001.

A note on future research in segmentation techniques applied to neurology,

cardiology,mammography and pathology.In:Advanced algorithmic approaches to medical image segmentation:state-of-the-art application in cardiology,neurology,mammography and pathology.Springer-Verlag New York,Inc.,pp.559–572.

Tsai,A.,Yezzi,A.,Wells,W.,Tempany,C.,Tucker,D.,Fan,A.,Grimson,W.,Willsky,

A.,2003.A shape-based approach to the segmentation of medical imagery using

level sets.IEEE Trans.Med.Imag.22(2),137–154.

üzümcü,M.,Frangi,A.,Sonka,M.,Reiber,J.,Lelieveldt,B.,2003.ICA vs.PCA active appearance models:application to cardiac MR segmentation.In:Proceedings of Medical Image Computing and Computer-Assisted Intervention(MICCAI),pp.

451–458.

üzümcü,M.,van der Geest,R.,Swingen,C.,Reiber,J.,Lelieveldt,B.,2006.Time continuous tracking and segmentation of cardiovascular magnetic resonance images using multidimensional dynamic programming.Invest.Radiol.41(1), 52–62.

van Assen,H.,Danilouchkine,M.,Frangi,A.,Ordas,S.,Westenberg,J.,Reiber,J., Lelieveldt, B.,2006.SPASM:a3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data.Med.Image Anal.10(2),286–303.

van der Geest,R.,Jansen,E.,Buller,V.,Reiber,J.,1994.Automated detection of left ventricular epi-and endocardial contours in short-axis MR images.In: Computers in Cardiology,Bethesda,MD,USA,pp.33–36.Viola,P.,Jones,M.,2001.Rapid object detection using a boosted cascade of simple features.In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,pp.511–518.

Waiter,G.,McKiddie,F.,Redpath,T.,Semple,S.,Trent,R.,1999.Determination of normal regional left ventricular function from cine-MR images using a semi-automated edge detection method.Magn.Reson.Imag.17(1),99–107. Wang,Y.,Jia,Y.,2006.Segmentation of the left ventricle from cardiac MR images based on degenerated minimal surface diffusion and shape priors.In: Proceedings of the18th International Conference on Pattern Recognition (ICPR),vol.4,pp.671–674.

Weng,J.,Singh,A.,Chiu,M.,1997.Learning-based ventricle detection from cardiac MR and CT images.IEEE Trans.Med.Imag.16(4),378–391.

Wijnhout,J.,Hendriksen,D.,Assen,H.V.,der Geest,R.V.,2009.LV challenge LKEB contribution:fully automated myocardial contour detection.In:MICCAI2009 Workshop on Cardiac MR Left Ventricle Segmentation Challenge.MIDAS Journal.

Xu,C.,Pham,D.L.,Prince,J.L.,2000.Medical image segmentation using deformable models.In:Handbook of Medical Imaging.Medical Image Processing and Analysis,vol.2.SPIE Press,pp.129–174.

Xu,C.,Prince,J.,1998.Snakes,shapes,and gradient vector?ow.IEEE Transactions on Image Processing7(3),359–369.

Yan,P.,Sinusas, A.,Duncan,J.S.,2007.Boundary element method-based regularization for recovering of LV deformation.Med.Image Anal.11(6), 540–554.

Yeh,J.,Fua,J.,Wua,C.,Lina,H.,Chaib,J.,2005.Myocardial border detection by branch-and-bound dynamic programming in magnetic resonance images.

Comput.Methods Programs Biomed.79(1),19–29.

Yezzi,A.,Kichenassamy,S.,Kumar,A.,Olver,P.,Tannenbaum,A.,1997.A geometric snake model for segmentation of medical imagery.IEEE Trans.Med.Imag.16

(2),199–209.

Zambal,S.,Hladuvka,J.,Bühler,K.,2006.Improving segmentation of the left ventricle using a two-component statistical model.In:Proceedings of Medical Image Computing and Computer-Assisted Intervention(MICCAI).LNCS,pp.

151–158.

Zhang,H.,Wahle,A.,Johnson,R.,Scholz,T.,Sonka,M.,2010.4-D cardiac MR image analysis:left and right ventricular morphology and function.IEEE Trans.Med.

Imag.29(2),350–364.

Zhuang,X.,Hawkes,D.,Crum,W.,Boubertakh,R.,Uribe,S.,Atkinson,D.,Batchelor, P.,Schaeffter,T.,Razavi,R.,Hill,D.,2008.Robust registration between cardiac MRI images and atlas for segmentation propagation.In:Society of Photo-Optical Instrumentation Engineers(SPIE)Conference,p.691408.

Zhukov,L.,Bao,J.,Guskov,I.,Wood,J.,Breen,D.,2002.Dynamic deformable models for3D MRI heart segmentation.In:Proceedings of SPIE.Medical Imaging,pp.

1398–1405.

184 C.Petitjean,J.-N.Dacher/Medical Image Analysis15(2011)169–184

尊重的素材

尊重的素材（为人处世） 思路 人与人之间只有互相尊重才能友好相处 要让别人尊重自己，首先自己得尊重自己 尊重能减少人与人之间的摩擦 尊重需要理解和宽容 尊重也应坚持原则 尊重能促进社会成员之间的沟通 尊重别人的劳动成果 尊重能巩固友谊 尊重会使合作更愉快 和谐的社会需要彼此间的尊重 名言 施与人，但不要使对方有受施的感觉。帮助人，但给予对方最高的尊重。这是助人的艺术，也是仁爱的情操。—刘墉 卑己而尊人是不好的，尊己而卑人也是不好的。———徐特立 知道他自己尊严的人，他就完全不能尊重别人的尊严。———席勒 真正伟大的人是不压制人也不受人压制的。———纪伯伦 草木是靠着上天的雨露滋长的，但是它们也敢仰望穹苍。———莎士比亚 尊重别人，才能让人尊敬。———笛卡尔 谁自尊，谁就会得到尊重。———巴尔扎克 人应尊敬他自己，并应自视能配得上最高尚的东西。———黑格尔 对人不尊敬，首先就是对自己的不尊敬。———惠特曼

每当人们不尊重我们时，我们总被深深激怒。然而在内心深处，没有一个人十分尊重自己。———马克·吐温 忍辱偷生的人，绝不会受人尊重。———高乃依 敬人者，人恒敬之。———《孟子》 人必自敬，然后人敬之；人必自侮，然后人侮之。———扬雄 不知自爱反是自害。———郑善夫 仁者必敬人。———《荀子》 君子贵人而贱己，先人而后己。———《礼记》 尊严是人类灵魂中不可糟蹋的东西。———古斯曼 对一个人的尊重要达到他所希望的程度，那是困难的。———沃夫格纳 经典素材 1元和200元 （尊重劳动成果） 香港大富豪李嘉诚在下车时不慎将一元钱掉入车下，随即屈身去拾，旁边一服务生看到了，上前帮他拾起了一元钱。李嘉诚收起一元钱后，给了服务生200元酬金。 这里面其实包含了钱以外的价值观念。李嘉诚虽然巨富，但生活俭朴，从不挥霍浪费。他深知亿万资产，都是一元一元挣来的。钱币在他眼中已抽象为一种劳动，而劳动已成为他最重要的生存方式，他的所有财富，都是靠每天20小时以上的劳动堆积起来的。200元酬金，实际上是对劳动的尊重和报答，是不能用金钱衡量的。 富兰克林借书解怨 （尊重别人赢得朋友）

那一刻我感受到了幸福_初中作文

那一刻我感受到了幸福 本文是关于初中作文的那一刻我感受到了幸福，感谢您的阅读！ 每个人民的心中都有一粒幸福的种子，当它拥有了雨水的滋润和阳光的沐浴，它就会绽放出最美丽的姿态。那一刻，我们都能够闻到幸福的芬芳，我们都能够感受到幸福的存在。 在寒假期间，我偶然在授索电视频道，发现(百家讲坛)栏目中大学教授正在解密幸福，顿然引起我的好奇心，我放下了手中的遥控器，静静地坐在电视前，注视着频道上的每一个字，甚至用笔急速记在了笔记本上。我还记得，那位大学教授讲到了一个故事：一位母亲被公司升职到外国工作，这位母亲虽然十分高兴，但却又十分无奈，因为她的儿子马上要面临中考了，她不能撇下儿子迎接中考的挑战，于是她决定拒绝这了份高薪的工作，当有人问她为什么放弃这么好的机会时，她却毫无遗憾地说，纵然我能给予儿子最贵的礼物，优异的生活环境，但我却无当给予他关键时刻的那份呵护与关爱，或许以后的一切会证明我的选择是正确的。听完这样一段故事，我心中有种说不出的感觉，刹那间，我仿拂感觉那身边正在包饺子的妈妈，屋里正在睡觉的爸爸，桌前正在看小说的妹妹给我带来了一种温馨，幸福感觉。正如教授所说的那种解密幸福。就要选择一个明确的目标，确定自已追求的是什么，或许那时我还不能完全诠释幸福。 当幸福悄悄向我走来时，我已慢慢明白，懂得珍惜了。 那一天的那一刻对我来说太重要了，原本以为出差在外的父母早已忘了我的生日，只有妹妹整日算着日子。我在耳边唠叨个不停，没想到当日我失落地回到家中时，以为心中并不在乎生日，可是眼前的一切，让我心中涌现的喜悦，脸上露出的微笑证明我是在乎的。

爸爸唱的英文生日快乐歌虽然不是很动听，但爸爸对我的那份爱我听得很清楚，妈妈为我做的长寿面，我细细的品尝，吃出了爱的味道。妹妹急忙让我许下三个愿望，嘴里不停的唠叨：我知道你的三个愿望是什么?我问：为什么呀!我们是一家人，心连心呀!她高兴的说。 那一刻我才真正解开幸福的密码，感受到了真正的幸福，以前我无法理解幸福，即使身边有够多的幸福也不懂得欣赏，不懂得珍惜，只想拥有更好更贵的，其实幸福比物质更珍贵。 那一刻的幸福就是爱的升华，许多时候能让我们感悟幸福不是名利，物质。而是在血管里涌动着的，漫过心底的爱。 也许每一个人生的那一刻，就是我们幸运的降临在一个温馨的家庭中，而不是降临在孤独的角落里。 家的感觉就是幸福的感觉，幸福一直都存在于我们的身边!

关于我的幸福作文八篇汇总

关于我的幸福作文八篇汇总 幸福在每个人的心中都不一样。在饥饿者的心中，幸福就是一碗香喷喷的米饭；在果农的心中，幸福就是望着果实慢慢成熟；在旅行者的心中，幸福就是游遍世界上的好山好水。而在我的心中，幸福就是每天快快乐乐，无忧无虑；幸福就是朋友之间互相帮助，互相关心；幸福就是在我生病时，母亲彻夜细心的照顾我。 幸福在世间上的每个角落都可以发现，只是需要你用心去感受而已。 记得有一次，我早上出门走得太匆忙了，忘记带昨天晚上准备好的钢笔。老师说了：“今天有写字课，必须要用钢笔写字，不能用水笔。”我只好到学校向同学借了。当我来到学校向我同桌借时，他却说：“我已经借别人了，你向别人借吧！”我又向后面的同学借，可他们总是找各种借口说：“我只带了一枝。”问了三四个人，都没有借到，而且还碰了一鼻子灰。正当我急的像热锅上的蚂蚁团团转时，她递给了我一枝钢笔，微笑的对我说：“拿去用吧！”我顿时感到自己是多么幸福！在我最困难的时候，当别人都不愿意帮助我的时候，她向我伸出了援手。 幸福也是无时无刻都在身旁。 当我生病的时候，高烧持续不退时，是妈妈在旁边细心

的照顾我，喂我吃药，甚至一夜寸步不离的守在我的床边，直到我苏醒。当我看见妈妈的眼睛布满血丝时，我的眼眶在不知不觉地湿润了。这时我便明白我有一个最疼爱我的妈妈，我是幸福的！ 幸福就是如此简单！不过，我们还是要珍惜眼前的幸福，还要给别人带来幸福，留心观察幸福。不要等幸福悄悄溜走了才发现，那就真的是后悔莫及了！ 这就是我拥有的幸福，你呢？ 悠扬的琴声从房间里飘出来，原来这是我在弹钢琴。优美的旋律加上我很强的音乐表现力让一旁姥爷听得如醉如痴。姥爷说我是幸福的，读了《建设幸福中国》我更加体会到了这一点。 儿时的姥爷很喜欢读书，但当时家里穷，据姥爷讲那时上学可不像现在。有点三天打鱼两天晒网，等地里农活忙了太姥爷就说：“别去念书了，干地里的活吧。”干活时都是牛马拉车，也没机器，效率特别低。还要给牲口拔草，喂草，拾柴火，看书都是抽空看。等农闲时才能背书包去学校，衣服更是老大穿了，打补丁老二再接着穿，只有盼到过年时才有能换上件粗布的新衣服。写字都是用石板，用一次擦一次，那时还没有电灯，爱学习的姥爷在昏暗的煤油灯下经常被灯火不是烧了眉毛就是燎了头发。没有电灯更没有电视，没有电视更没有见过钢琴，只知道钢琴是贵族家用的。

小学生作文《感悟幸福》范文五篇汇总

小学生作文《感悟幸福》范文五篇 小草说,幸福就是大地增添一份绿意;阳光说,幸福就是撒向人间的温暖;甘露说,幸福就是滋润每一个生命。下面是为大家带来的有关幸福650字优秀范文，希望大家喜欢。 感悟幸福650字1 生活就像一部壮丽的交响曲，它是由一篇一篇的乐章组成的，有喜、有怒、有哀、有乐。每一个人都有自己丰富多彩的生活，我也有自己的生活。我原本以为，吃可口的牛排，打电脑游戏，和朋友开心玩乐就是幸福。可是，我错了，幸福并不仅仅如此。 记得有一次，我放学回到家里放下书包就拿起一包饼干来吃。吃着吃着，突然我觉得牙齿痛了起来，而且越来越痛，痛得我连饼干也咬不动了。我放下饼干，连忙去拿了一面镜子来看。原来这又是那一颗虫牙在“作怪”。“哎哟哟，哎哟哟，痛死我了……”我不停地说着。渐渐地，那牙疼得越来越厉害，疼得我坐立不安，直打滚。后来在妈妈的陪伴下去了医院，治好了那颗虫牙。跨出医院大门时，我觉得心情出奇的好，天空格外的蓝，路边的樟树特别的绿，看什么都顺眼，才猛然一悟，幸福是简单而平凡的，身体健康就是一种幸福! 这学期我发现我的英语退步了，我决定要把这门功课学好，于是，我每天回

家做完作业后，都抽出半小时时间复习英语，在课上也听得特别认真，一遇到不懂的题目主动请教老师。经过一段时间的努力，终于，在上次考试的时候，我考了97分。妈妈表扬了我，我心里美滋滋的。我明白了经过自己的努力享受到成功的喜悦，这也是一种幸福。 …… 每个人都无一例外的渴望幸福。不同的人有不同的感受，其实，幸福就是那种能在平凡中寻找欢乐、能在困境中找到自信的一种心境。同学们，幸福其实很简单，就在我们的身边，触手可及。用心去认真地品味吧，它一直未曾离开我们身边! 感悟幸福650字2 有的人认为幸福就是腰缠万贯，有的人认为幸福就是找到意中人，“采菊东篱下，悠然见南山”是陶渊明对邪恶幸福，“从明天起做一个幸福人，喂马、劈柴、周游世界。从明天起，关心蔬菜和粮食，我有一所房子，面朝大海，春暖花开。”这是海子的幸福。一千种人就有一千种对幸福的理解。 我对幸福的理解就是幸福使简单而平凡的，是无处不在的! 我的牙疼得奇怪而顽强不是这颗牙疼就是那颗牙疼;不是吃冷的疼就是吃热

关于以幸福为话题的作文800字记叙文5篇

关于以幸福为话题的作文800字记叙文5篇 ----WORD文档，下载后可编辑修改---- 下面是作者为各位家长学生收集整理的作文（日记、观后感等）范本，欢迎借鉴参考阅读，您的努力学习和创新是为了更美好的未来，欢迎下载！ 以幸福为话题的作文800字记叙文1： 那是我生病后的第三天，妈妈从早上五点就起来为我准备早点。她蹑手蹑脚地走着“针步”，下楼煮早点，“啪”的一声，妈妈打开了煤气。在拿肉丝，打鸡蛋的她全然不知我正躲在楼梯口“监视”着她的一举一动。不一会儿，蛋炒好了。 她开始切肉丝，一不小心，妈妈的手指切破皮了，鲜血正一滴一滴地流下来，为了不影响我的睡眠，她把手指放在嘴里吸了一下，坚持把剩下的肉丝切完。 此时的我，心中犹如打翻了五味瓶，眼里的泪像断了线的珍珠般掉了下来，我再也忍不住了，一个劲地冲到妈妈面前，她赶紧把手背了过去，生怕让我知道了什么。 她吃惊地问我：“妈妈太吵了，吵到你了?”“不，不，没有”她见我这么早起来就让我再回去补个觉。我关心地问：“妈，你的手没事吧?”她吱唔着说：“没事，擦破点皮，不碍事!”我仔细地帮她清洗了伤口，贴了一片创可贴。 吃饭时，妈妈一直地往我碗里夹肉，“孩子，病刚好，多吃点!”可是我见她始终都没吃一块肉。我也夹了两块放在她的碗里。“儿子懂事了，你自己快点吃吧!补身体要紧!”我冲她点点头笑了笑，“嗯。” 这就是幸福，一份简简单单的幸福!我祈祷这幸福能伴我成长。 以幸福为话题的作文800字记叙文2： 在我眼中，成长就是记录我们长大过程中一点一滴的小事情的，而幸福就在这点点滴滴中。 在我的成长记忆中，永不磨灭的是2017年11月的一天。妈妈要去云南，妈妈早上四点半要到指定地点集合，这么早，妈妈要两三点就起来，可是最近我咳嗽比较严重，所以天天给我煮萝卜汤喝。 “叮铃铃，叮铃铃”闹钟叫了起来，把我从睡梦中吵醒，一醒来，去找妈妈，

尊重议论文

谈如何尊重人尊重他人，我们赢得友谊；尊重他人，我们收获真诚；尊重他人，我们自己也 获得尊重；相互尊重，我们的社会才会更加和谐. ——题记 尊重是对他人的肯定，是对对方的友好与宽容。它是友谊的润滑剂，它是和谐的调节器， 它是我们须臾不可脱离的清新空气。“主席敬酒，岂敢岂敢？”“尊老敬贤，应该应该！”共和 国领袖对自己老师虚怀若谷，这是尊重；面对许光平女士，共和国总理大方的叫了一 声“婶婶”，这种和蔼可亲也是尊重。 尊重不仅会让人心情愉悦呼吸平顺，还可以改变陌生或尖锐的关系，廉颇和蔺相如便是 如此。将相和故事千古流芳:廉颇对蔺相如不满，处处使难，但蔺相如心怀大局，对廉颇相 当的尊重，最后也赢得了廉颇的真诚心，两人结为好友，共辅赵王，令强秦拿赵国一点办法 也没有。蔺相如与廉颇的互相尊重，令得将相和的故事千百年令无数后人膜拜。 现在，给大家举几个例子。在美国，一个颇有名望的富商在散步 时，遇到一个瘦弱的摆地摊卖旧书的年轻人，他缩着身子在寒风中啃着发霉的面包。富 商怜悯地将8美元塞到年轻人手中，头也不回地走了。没走多远，富商忽又返回，从地摊上 捡了两本旧书，并说：“对不起，我忘了取书。其实，您和我一样也是商人！”两年后，富商 应邀参加一个慈善募捐会时，一位年轻书商紧握着他的手，感激地说：“我一直以为我这一生 只有摆摊乞讨的命运，直到你亲口对我说，我和你一样都是商人，这才使我树立了自尊和自 信，从而创造了今天的业绩??”不难想像，没有那一 句尊重鼓励的话，这位富商当初即使给年轻人再多钱，年轻人也断不会出现人生的巨变， 这就是尊重的力量啊 可见尊重的量是多吗大。大家是不是觉得一个故事不精彩，不够明确尊重的力量，那再 来看下一个故事吧！ 一家国际知名的大企业，在中国进行招聘，招聘的职位是该公司在中国的首席代表。经 过了异常激烈的竞争后，有五名年轻人，从几千名应聘者中脱颖而出。最后的胜出者，将是 这五个人中的一位。最后的考试是一场面试，考官们都 作文话题素材之为人处世篇：尊重 思路 人与人之间只有互相尊重才能友好相处 要让别人尊重自己，首先自己得尊重自己 尊重能减少人与人之间的摩擦 尊重需要理解和宽容 尊重也应坚持原则 尊重能促进社会成员之间的沟通 尊重别人的劳动成果 尊重能巩固友谊 尊重会使合作更愉快 和谐的社会需要彼此间的尊重 名言 施与人，但不要使对方有受施的感觉。帮助人，但给予对方最高的尊重。这是助人的艺 术，也是仁爱的情操。———刘墉 卑己而尊人是不好的，尊己而卑人也是不好的。———徐特立 知道他自己尊严的人，他就完全不能尊重别人的尊严。———席勒 真正伟大的人是不压制人也不受人压制的。———纪伯伦 草木是靠着上天的雨露滋长的，但是它们也敢仰望穹苍。———莎士比亚

最新整理高中关于幸福的议论文800字范文3篇

最新整理高中关于幸福的议论文800字范文3篇 范文一 什么是幸福?当我把一个棒棒糖递给六岁的邻居小妹妹时，她满足的笑容告诉我，这是她的幸福。当我轻轻地走过妹妹的写字台时，我瞥见埋在桌上的妹妹的僵硬的表情。我笑笑，走近，她抬头，水汪汪的眼睛望着我，似乎带着某种渴求。我说：出去玩吧!她笑了，蹦蹦跳跳地跑了出去。我诧异，这么真诚的笑。玩耍是她的幸福。 暑假到了，马上面临实习的哥哥回来了。可没过几天，就不见人影了，好容易盼他回来，暑假也结束了。他说他去了内蒙的好多地方。我关切的问他累吗?他说：累啊!随后又骄傲地说：“可是我学会了许多东西，我相信那对我以后的人生路是有帮助的。”我笑，大声地喊：哥，你是我的榜样。在他看来，他的暑假是充实的，他是幸福的! 夜幕降临，繁星点点。隔着一层帘，我看见常年劳作的父亲坐在那里，默默地吸着一支烟。灯光打在他的脸上，我看不清他的表情，只有那斑白的鬓角依稀可见。父亲真的老了，每天早出晚归来支撑这个家，他一定很累了。眼泪盈满了眼眶，最后还是不争气的流了下来……“咳、、咳、、”一阵剧烈的咳嗽声传来。我擦干眼泪，走到父亲旁边，父亲把那支烟熄灭，慈祥的笑笑，说：爸爸老了，不中用了。我说：没有啊!父女两开怀的笑了，笑声混着一个个烟圈飘向远方……我问父亲：爸，这么多年付出，这么多年劳作，你幸福吗?他坚定地告诉我，幸福!他说：“只要你们开开心心快快乐乐地成长，我做的一切都值得。”他又说：“霞，好好读书，爸爸赚钱供你上大学，我还没老呢，至少还能干XX年，20年……然后是一片寂静，我和父亲看着远方，那里有希望。 年迈的姥姥是家里的大长辈，他常常念叨：平安就是福。那也许是经历了人生的酸甜苦辣后的感悟吧!每逢新春，一大家人在姥姥家围着看电视时，那应该是她的幸福吧! 幸福是什么?它不是你一个人拥有一座豪宅，它是一家人在并不宽敞的屋子里谈笑风生。它不是你一个人有拥山珍海味，它是一家人和和乐乐的吃一些普通

感受幸福作文(15篇)

感受幸福作文(15篇) 感受幸福作文第1篇： 幸福是什么？这是许多同学要问的问题。 很小的时候，我就明白钱能够买来一大盒巧克力；钱能够买来玩具汽车；钱能够买许多的美丽的洋娃娃；钱能够买来一个大楼…… 我以为有钱就是幸福。 倡我错了，钱虽然能够买来一屋子巧克力，但买了甜蜜，钱虽然能买到房子，可是却买来家庭幸福；钱虽然能买来药，可是却买来健康，钱虽然能买来闹钟，可是买来时间……那时，我又明白了有钱必须幸福。 以前，我总是为了一条连衣裙而朝思暮想，盼望有一天能够穿上裙子，去放风筝。那时候，我以为拥有就是幸福。 最终有一天，妈妈给我买了这条连衣裙，我高兴的一宿都没有睡觉。可是几天的新鲜劲没有了，穿上裙子后，我并没有什么改变，依然是一个黄毛丫头。于是把它扔到箱子里。几个月后，我又把它翻出来，可是已经小了，穿下了。我又明白了，虽然裙子很美，但都是暂时的，完美的时光总是转瞬消失。 “幸福是什么？”我依然没有感受到。 几年后，我在街上看到了一对耄耋老人，他们虽然履蹒跚，可是互相搀扶，有时抬头看看天上的云卷云舒，有时望望西天如血的残阳，她们脸上洋溢着的是满足和幸福。 噢，我明白幸福就是真情。虽然他们很穷，可是他们很

相爱。他们彼此珍惜，从感叹世界对他们的公平。往往有的有钱人，他们虽然很有钱，可是他们并幸福，因为他们的心总是被金钱和权势所占据了，根本享受了这天伦之乐。 幸福其实很简单，就是和爸爸、妈妈吃一顿饭，和他在一齐聊聊天。 感受幸福作文第2篇： 夜，悄悄地打开了黑暗，散布着一如既往的宁静，天上的繁星披上了闪装，正对着我的眼，似乎害怕我听到它们之间的悄悄话。 知何时，甘寂寞的虫儿起劲地奏起了动听的乐曲，清凉的微风夹杂着泥土的芳香悄悄地将白天的烦闷与喧嚣赶跑。夜，显得更加宁静而诗意了。 静静的，左思，右想，就这样静静地坐在楼顶上，感受着夜馈赠我的美妙。就连天上偶尔飘过的云朵，也像是怕惊动了夜的宁静，如绒毛在平静水面滑过般，显得那么轻柔而迷人。 今夜独处在空旷的夜空下，感受着夜带给我的美妙，幸福惬意溢满于心。原先自我一向以来苦苦追寻的幸福其实就在自我的身边。 以往，有人努力打拼，渴望生活富裕来获得幸福，可一辈子的艰辛拼搏使自我逐渐沦为金钱的奴隶，苦苦追寻的幸福也越寻越远，最终留给自我的是岁月无情地染白的头发。其实，幸福并非是追寻能得到的，幸福是一种感受，仅有用心感受身边的一切，你就能发现，幸福无处在，譬如，管贫

尊重他人的写作素材

尊重他人的写作素材 导读：——学生最需要礼貌 著名数学家陈景润回厦门大学参加 60 周年校庆，向欢迎的人们说的第一句话是：“我非常高兴回到母校，我常常怀念老师。”被人誉为“懂得人的价值”的著名经济学家、厦门大学老校长王亚南，曾经给予陈景润无微不至的关心和帮助。陈景润重返母校，首先拜访这位老校长。校庆的第三天，陈景润又出现在向“哥德巴赫猜想”进军的启蒙老师李文清教授家中，陈景润非常尊重和感激他。他还把最新发表的数学论文敬送李教授审阅，并在论文扉页上工工整整写了以下的字：“非常感谢老师的长期指导和培养——您的学生陈景润。”陈景润还拜访了方德植教授，方教授望着成就斐然而有礼貌的学生，心里暖暖的。 ——最需要尊重的人是老师 周恩来少年时在沈阳东关模范学校读书期间 , 受到进步教师高盘之的较大影响。他常用的笔名“翔宇”就是高先生为他取的。周恩来参加革命后不忘师恩 , 曾在延安答外国记者问时说：“少年时代我在沈阳读书 , 得山东高盘之先生教诲与鼓励 , 对我是个很大的 促进。” 停奏抗议的反思 ——没有礼仪就没有尊重 孔祥东是著名的钢琴演奏家。 1998 年 6 月 6 日晚，他在汕头

举办个人钢琴独奏音乐会。演出之前，节目主持人再三强调，场内观众不要随意走动，关掉 BP 机、手提电话。然而，演出的过程中，这种令人遗憾的场面却屡屡发生：场内观众随意走动， BP 机、手提电话响声不绝，致使孔祥东情绪大受干扰。这种情况，在演奏舒曼作品时更甚。孔祥东只好停止演奏，静等剧场安静。然而，观众还误以为孔祥东是在渴望掌声，便报以雷鸣般的掌声。这件事，令孔祥东啼笑皆非。演出结束后，孔祥东说：有个 BP 机至少响了 8 次，观众在第一排来回走动，所以他只得以停奏抗议。 “礼遇”的动力 ——尊重可以让人奋发 日本的东芝公司是一家著名的大型企业，创业已经有 90 多年的历史，拥有员工 8 万多人。不过，东芝公司也曾一度陷入困境，土光敏夫就是在这个时候出任董事长的。他决心振兴企业，而秘密武器之一就是“礼遇”部属。身为偌大一个公司的董事长，他毫无架子，经常不带秘书，一个人步行到工厂车间与工人聊天，听取他们的意见。更妙的是，他常常提着酒瓶去慰劳职工，与他们共饮。对此，员工们开始都感到很吃惊，不知所措。渐渐地，员工们都愿意和他亲近，他赢得了公司上下的好评。他们认为，土光董事长和蔼可亲，有人情味，我们更应该努力，竭力效忠。因此，土光上任不久，公司的效益就大力提高，两年内就把亏损严重、日暮途穷的公司重新支撑起来，使东芝成为日本最优秀的公司之一。可见，礼，不仅是调节领导层之间关

感悟幸福(作文)(20篇)

感悟幸福(作文)(20篇) 篇一篇的乐章组成的，喜、怒、哀、乐。每一个人都自我丰富多彩的生活，我也自我的生活。我原本以为，吃可口的牛排，打电脑游戏，和朋友开心玩乐就是幸福。可是，我错了，幸福并不仅仅如此。 记得一次，我放学回到家里放下书包就拿起一包饼干来吃。吃着吃着，突然我觉得牙齿痛了起来，并且越来越痛，痛得我连饼干也咬不动了。我放下饼干，连忙去拿了一面镜子来看。原先这又是那一颗虫牙在“作怪”。“哎哟哟，哎哟哟，痛死我了”我不停地说着。渐渐地，那牙疼得越来越厉害，疼得我坐立不安，直打滚。之后在妈妈的陪伴下去了医院，治好了那颗虫牙。跨出医院大门时，我觉得心境出奇的好，天空格外的蓝，路边的樟树异常的绿，看什么都顺眼，才猛然一悟，幸福是简单而平凡的，身体健康就是一种幸福！ 这学期我发现我的英语退步了，我决定要把这门功课学好，于是，我每一天回家做完作业后，都抽出半小时时间复习英语，在课上也听得异常认真，一遇到不懂的题目主动请教教师。经过一段时间的努力，最终，在上次考试的时候，我考了97分。妈妈表扬了我，我心里美滋滋的。我明白了经过自我的努力享受到成功的喜悦，这也是一种幸福。

每个人都无一例外的渴望幸福。不一样的人不一样的感受，其实，幸福就是那种能在平凡中寻找欢乐、能在困境中找到自信的一种心境。同学们，幸福其实很简单，就在我们的身边，触手可及。用心去认真地品味吧，它一向未曾离开我们身边！ 感悟幸福(作文) 第8篇： 幸福无时没，幸福无处不在。儿时，你的幸福就是当你跌倒失声痛苦时，母亲抱起你，拍打着你背时的那句“别哭。”;成年时，你的幸福就是在你结婚时你身旁的那位拉起你的手问你的那句“你愿意吗”;老年时，你的幸福就是在椅子背后孙子的那句“奶奶我帮你敲背!”。家庭中，幸福是一家子的和乐融融;学校里，幸福是同学间的互相竞争;赛场上，幸福是队友间的默契配合。 然而，幸福不全然是唾手可得的，想不付出任何代价而得到幸福，那是神话。张志新说;“苦换来的是知识真理，坚持真理，就应自觉的欣然理解痛苦，那时，也仅那时，痛苦才将化为幸福。”仅心身遭受过痛苦的洗涤，幸福才会来到，痛得越深，苦得越彻，幸福得就越甜。鲁迅说：“穿掘到灵魂的深处，使人受了精神的苦刑而得到的创伤，又即从这得伤和养伤的愈合中，得到苦的涤除，而上了苏生的路。”从而得到了幸福的真谛。拉罗什夫科说过：“幸福后面是灾祸，灾祸后面是幸福。”你想成为幸福的人吗愿你首先吃得

尊重_议论文素材

尊重_议论文素材 "礼遇"的动力 --尊重可以让人奋发 日本的东芝公司是一家著名的大型企业，创业已经有90 多年的历史，拥有员工8 万多人。不过，东芝公司也曾一度陷入困境，土光敏夫就是在这个时候出任董事长的。他决心振兴企业，而秘密武器之一就是"礼遇"部属。身为偌大一个公司的董事长，他毫无架子，经常不带秘书，一个人步行到工厂车间与工人聊天，听取他们的意见。更妙的是，他常常提着酒瓶去慰劳职工，与他们共饮。对此，员工们开始都感到很吃惊，不知所措。渐渐地，员工们都愿意和他亲近，他赢得了公司上下的好评。他们认为，土光董事长和蔼可亲，有人情味，我们更应该努力，竭力效忠。因此，土光上任不久，公司的效益就大力提高，两年内就把亏损严重、日暮途穷的公司重新支撑起来，使东芝成为日本最优秀的公司之一。可见，礼，不仅是调节领导层之间关系的纽带，也是调节上下级之间关系，甚至和一线工人之间关系的纽带。世界知识产权日 --尊重知识 在2000 年10 月召开的世界知识产权组织第35 届成员国大会上，我国提议将 4 月26 日定为"世界知识产权日"。这个提案经世界知识产权组织成员国大会得到了确定。2001 年4 月26 日成为第一个"世界知识产权日"。这是我国尊重知识的具体表现。 屠格涅夫与乞丐 --尊重比金钱更重要 俄罗斯文豪屠格涅夫一日在镇上散步，路边有一个乞丐伸手向他讨钱。他很想有所施与，从口袋掏钱时才知道没有带钱袋。见乞丐的手伸得高高地等着，屠格涅夫面有愧色，只好握着乞丐的手说："对不起，我忘了带钱出来。"乞丐笑了，含泪说："不，我宁愿接受您的握手。" 孙中山尊重护士 --尊重不分社会地位 有一天，孙中山先生病了，住院治疗。当时，孙中山已是大总统、大元帅了。但是，他对医务人员很尊重，对他们讲话很谦逊。平时，无论是早晨或是晚间，每当接到护士送来的药品，他总是微笑着说声"谢谢您"，敬诚之意溢于言辞。 1925 年孙中山患肝癌，弥留之际，当一位护理人员为他搬掉炕桌时，孙中山先生安详地望着她，慈祥地说："谢谢您，您的工作太辛苦了，过后您应该好好休息休息，这阵子您太辛苦了! "听了这话，在场的人都泣不成声。 毛泽东敬酒 --敬老尊贤是一种美德 1959 年6 月25 日，毛泽东回到离别30 多年的故乡韶山后，请韶山老人毛禹珠来吃饭，并特地向他敬酒。毛禹珠老人说："主席敬酒，岂敢岂敢! "毛泽东接着说："敬老尊贤，应该应该。" 周恩来不穿拖鞋接待外宾 --衣着整齐体现对人的尊重 周恩来晚年病得很重，由于工作的需要，他还要经常接待外宾。后来，他病得连脚都肿起来了，原先的皮鞋、布鞋都不能穿，他只能穿着拖鞋走路，可是，有些重要的外事活动，他还是坚持参加。他身边的工作人员出于对总理的爱护和关心，对他说："您就穿着拖鞋接待外

关于尊重的论点和论据素材

关于尊重的论点和论据素材 关于尊重的论点 1.尊重需要理解和宽容。 2.尊重也应该坚持原则。 3.尊重知识是社会进步的表现。 4.尊重别人就要尊重别人的劳动。 5.尊重人才是社会发展的需要。 6.人与人之间需要相互尊重。 7.只有尊重别人才会受到别人的尊重。 8.尊重能促进人与人之间的沟通。 9.我们应该养成尊重他人的习惯。 10.对人尊重，常会产生意想不到的善果。 关于尊重的名言 1.仁者必敬人。《荀子》 2.忍辱偷生的人决不会受人尊重。高乃依 3.尊重别人的人不应该谈自己。高尔基 4.尊重别人，才能让人尊敬。笛卡尔 5.谁自尊，谁就会得到尊重。巴尔扎克 6.君子贵人而贱己，先人而后己。《礼记》 7.卑己而尊人是不好的，尊己而卑人也是不好的。徐特立 8.对人不尊敬，首先就是对自己的不尊敬。惠特曼

9.为人粗鲁意味着忘记了自己的尊严。车尔尼雪夫斯基 10.对人不尊敬的人，首先就是对自己不尊重。陀思妥耶夫斯基 11.对于应尊重的事物，我们应当或是缄默不语，或是大加称颂。尼采 12.尊重老师是我们中华民族的传统美德，我们每一个人都不应该忘记。xx 13.尊重劳动、尊重知识、尊重人才、尊重创造。《xx 大报告》 14.对别人的意见要表示尊重。千万别说：你错了。卡耐基 15.尊重人才，培养人才，是通用电器长久不败的法宝。杰克韦尔奇 16.君子之于人也，当于有过中求无过，不当于无过中求有过。程颐 17.施与人，但不要使对方有受施的感觉。帮助人，但给予对方最高的尊重。这是助人的艺术，也是仁爱的情操。刘墉 18.要尊重每一个人，不论他是何等的卑微与可笑。要记住活在每个人身上的是和你我相同的性灵。叔本华 19.要喜欢我们所不尊重的人是很难的;但要喜欢

感受幸福作文范文5篇

感受幸福作文范文5篇 幸福是一种心灵的振颤。它像会倾听音乐的耳朵一样，需要不断地训练。下面给大家了感受幸福范文5篇，仅供参考。 我曾经填了一份读者反馈表，得了个二等奖，奖品是三百元钱。拿着那张三百元汇款单回家，我却不大开心。在路上，我想起了从前看过的一部电影，片名好像是《小鞋子》。 主人公小阿里在参加比赛时，一心只想跑第三名，以拿到梦寐以求的奖品——一双小鞋子。他想让妹妹穿着它，而不是光着脚去上学。 他被别人推倒了又爬起来再跑，情急之下，竟然冲到了第一名。 老师欣喜若狂地祝贺他，主办单位安排大官和他合影，阿里却难过地流下了眼泪。 尽管冠军有着很高的荣誉，能得到更为丰富的奖品，但那不是阿里最需要的，他需要那双并不漂亮的小鞋子。结果，他不仅没有得到，反而连仅有的一双鞋子也跑坏了。

在那个时候，我们或许可以这样解释幸福：幸福就是得到你急需的东西。 一个吃斋念佛的饥饿的人，面对着驰名中外的北京烤鸭，是不会有幸福感的，因为那不是他需要的，他急需的，可能只是两个馒头，一盘素炒豆芽而已；独自住在乡下的老人，总是能收到城里儿子寄来的钱物，很让乡亲们羡慕，但老人最想要的不是这些，而是儿孙膝下承欢的天伦之乐；在炎炎烈日下赶路的人，急需的是一片树阴，一阵凉风；一把扇子，一眼清凉的泉水，而不是一件名贵的貂皮大衣；大海是富饶的，它哺育了各种各样的生物，珍藏着无数的宝藏和财富。但在海上迷航的人，急需的却是普普通通的淡水。 聪明的你，可能已经猜到， ___不开心了。是的，我不需要三百元钱，钱我家里人会给我。我原本只想得个三等奖，三等奖的奖品是一百元钱。我想留为己用，不想让家里人知道。尽管这样做很不合理，但我还是想，因为我想。 幸福，是团聚；是分享快乐；是帮助他人；是朋友团结互助；是一个美梦；是……幸福无处不在，只要用心体会，就能感受到幸福的存在。

关于学会尊重的高中作文1000字以上_作文素材

关于学会尊重的高中作文1000字以上 尊重是一杯清茶，只有真正懂它的人才能忽略它的寡淡，品出它深处的热烈。下面橙子为大家搜集整理有关学会尊重的高中作文，希望可以帮助到大家! 高中作文学会尊重曾经听说这样一个故事： 一位商人看到一个衣衫破烂的铅笔推销员，顿生一股怜悯之情。他不假思索地将10元钱塞到卖铅笔人的手中，然后头也不回地走开了。走了没几步，他忽然觉得这样做不妥，于是连忙返回来，并抱歉地解释说自己忘了取笔，希望不要介意。最后，他郑重其事地说：“您和我一样，都是商人。” 一年之后，在一个商贾云集、热烈隆重的社交场合，一位西装革履、风度翩翩的推销商迎上这位商人，不无感激地自我介绍道：“您可能早已忘记我了，而我也不知道您的名字，但我永远不会忘记您。您就是那位重新给了我自尊和自信的人。我一直觉得自己是个推销铅笔的乞丐，直到您亲口对我说，我和您一样都是商人为止。” 没想到商人这么—句简简单单的话，竟使一个不无自卑的人顿然树立起自尊，使—个处境窘迫的人重新找回了自信。正是有了这种自尊与自信，才使他看到了自己的价值和优势，终于通过努力获得了成功。不难想象，倘若当初没有那么—句尊重鼓励的话，纵然给他几千元也无济于事，断不会出现从自认乞丐到自信自强的巨变，这就是尊1 / 7

重，这就是尊重的力量! 尊重，是—种修养，一种品格，一种对别人不卑不亢的平等相待，一种对他人人格与价值的的充分肯定。任何人都不可能尽善尽美，完美无缺，我们没有理由以高山仰止的目光审视别人，也没有资格用不屑一顾的神情去嘲笑他人。假如别人在某些方面不如自己，我们不能用傲慢和不敬去伤害别人的自尊;假如自己在有些地方不如他人，我们不必以自卑或嫉妒去代替理应有的尊重。一个真正懂得尊重别人的人，必然会以平等的心态、平常的心情、平静的心境，去面对所有事业上的强者与弱者、所有生活中的幸运者与不幸者。 尊重是一缕春风，一泓清泉，一颗给人温暖的舒心丸，一剂催人奋进的强心剂。它常常与真诚、谦逊、宽容、赞赏、善良、友爱相得益彰，与虚伪、狂妄、苛刻、嘲讽、凶恶、势利水火不容。给成功的人以尊重，表明了自己对别人成功的敬佩、赞美与追求;表明了自己对别人失败后的同情、安慰与鼓励。只有要尊重在，就有人间的真情在，就有未来的希望在，就有成功后的继续奋进，就有失败后的东山再起。 尊重不是盲目的崇拜，更不是肉麻的吹捧;不是没有原则的廉价逢迎，更不是没有自卑的低三下四。懂得了尊重别人的重要，并不等于学会了如何尊重别人，从这个意义上说，尊重他人也是一门学问，学会了尊重他人，就学会了尊重自己，也就学会和掌握了人生的一大要义。 2 / 7

感受幸福作文600字叙事 感受幸福作文600字初二

感受幸福作文600字叙事感受幸福作文600字初二 对于我们来说，什么是幸福？幸福就是一家人整整齐齐的坐在一起吃一顿饭，幸福就是自己在乎的人没病没痛，健健康康的.....今天小编和同学们分享几篇关于感受幸福的作文，希望帮到有需要的同学们。感受幸福作文600字【1】有人说，幸福是一种感觉，幸福是一种心境，幸福是一种体验，幸福，更是一种品格。忧伤时，贴心好友的一个拥抱是幸福；思乡时，电话那头响起的家人声音是幸福；听着音乐，啜着咖啡，悠然自在地写点感想是幸福……那么，朋友，你感受到了幸福了吗？幸福，有些人看来很渺远，有些人看来却近在咫尺。其实，世界并不缺少幸福，而是缺少发现幸福的眼睛。“猫吃鱼，狗吃肉，奥特曼打小怪兽”各求所需，心满意足，这，就是所谓的“幸福”。年轻的父母在逗弄牙牙学语的孩童；亲密爱人在携手漫步；父母端上热腾腾的饭菜；孩子熟睡后嘴角的微笑……这点点滴滴不都是幸福的剪影吗？幸福无处不在……有一次，妈妈烧了红烧鱼，刚要端上桌，我贪婪的目光就随着鱼的移动而移动。妈妈忙把好吃的鱼肚子夹给我，说：“来，吃吧！”“咦？你不是最爱吃这个吗？”我反问道。“现在不爱吃了，你吃吧！”妈妈说道。我拗不过妈妈，只好吃了。刹时间，幸福的感觉洋溢全身。只有在菜里放入了爱，才能让人感受到幸福。平凡的红烧鱼，不平凡的味道，不平凡的爱……幸福，往往是一个瞬间，只有有爱的人，才能发现幸福，感受幸福。朋友，感受你身边的幸福吧！幸福，无处不在……感受幸福作文600字【2】 人生若是一夜星空，幸福便是那耀眼的星星，琳琅满目。当你仔细观察时，你会发现，每一颗都独一无二。——题记 抬头仰望星空，看着那琳琅满目的星星，每一颗都独一无二。当我的眼神扫到最亮的那一颗时，我的思绪立马随着那颗星星穿越到过去。“神经病啊你，我都说过几百遍几千吧几万遍了，我的东西你别碰，你为什么就是讲不通听？好了，现在玻璃球打碎了，可以了吧？你舒服了吧？”扯着嗓子喊的是一个七岁的小女孩，她的对面站着一个五六岁的小女孩，那小女孩有着圆圆的脑袋，一头乌黑发亮的头发下有一双水灵灵的大眼睛，高挺的鼻梁下一张樱桃小嘴，模样甚是可爱。只是那眼眶湿润着，泪水如断了线的珍珠，不断地往外涌。她的小嘴蠕动了好几次，却始终没有说话，小女孩颤颤巍巍地向前走去，用颤抖的小手慢慢地拾起地下的玻璃碎片，转身离开。第二天清晨，当那个七岁的小女孩睁开眼时，就看到桌上有一个玻璃球和小纸条。纸条上写着歪歪扭扭的小字：姐姐对不起，我不是故意的，我只是觉得玻璃球很漂亮。现在我又帮你重新买了一个一模一样的玻璃球，希望姐姐不要生气了。童年的易满足心，那个小女孩果真没有生气了，她和那个小妹妹和好如初了。但偶然的一天，她却发现，玻璃球不是小妹妹打破的……原来是邻居的两岁小弟弟，看到那个玻璃球很漂亮，在伸手拿它时，因为太滑，玻璃球滚了下来……于是，她拿着自己最爱的零食和棒棒糖去和小妹妹道歉。没想到妹妹却如小大人一般说：“都过去了，你依旧是我的好姐姐，我永远是你的调皮妹妹……”回想到这里，我的眼眶早已红润。那个七岁的小女孩不就是我吗？那个小妹妹不就是自己的妹妹吗？顿时明白了妹妹的用意，顿时发现：幸福就像空气，无时无刻萦绕在我们身边，只是我们未曾发觉罢了。现在的我发觉了，也感受到了。人生若是一夜星空，幸福便是那耀眼的星星，琳琅满目。当你仔细观察时，你会发现，每一颗都独一无二。感受幸福作文600字【3】 什么是幸福？幸福是空气，它普遍的你随处都可以感受到它的存在；幸福是小鸟，它调皮的你怎么也抓不住它；幸福又是一杯水，平平淡淡才是真。幸福无时没有，幸福无处不在。儿时，你的幸福就是当你跌倒失声痛苦时，母亲抱起你，拍打着你背时的那句“别哭。”；成年时，你的幸福就是在你结婚时你身旁的那位拉起你的手问你的那句“你愿意吗？”；老年时，你的幸福就是在椅子背后孙子的那句“奶奶我帮你敲背！”。家庭中，幸福是一家子的和乐融融；学校里，幸福是同学间的互相竞争；赛场上，幸福是队友间的默契配合。然而，幸福不全然是唾手可得的，想不付出任何代价而得到幸福，那是神话。张x新说；“苦换来的是知识真理，坚持真理，就应自觉的欣然接受痛苦，那时，也只有那时，痛苦才将化为幸福。”

感悟幸福作文(精选10篇)

感悟幸福作文（精选10篇） 在日常生活或是工作学习中，大家都经常接触到作文吧，作文是由文字组成，经过人的思想考虑，通过语言组织来表达一个主题意义的文体。那么，怎么去写作文呢？以下是小编收集整理的感悟幸福作文（精选10篇），仅供参考，希望能够帮助到大家。 感悟幸福作文1 幸福是“临行密密缝，意恐迟迟归”的牵挂；幸福是“夜来风雨声，花落知多少”的恬谈；幸福是“先天下之忧而忧，后天下之乐而乐”的胸怀。 记得那是一个宁静的夜晚，家家户户家家户户都关了灯，可我家的灯却依旧工作着，因为我的作业还没有写完，懊恼的我使劲的捶打着桌子。妈妈听到了响声走了进来，亲切的说到：“怎么了乖女儿？”“都六点半了，我的作业还没有写完，我怎么不着急”，我气急败坏的回答。妈妈看到我无助的状态安慰到“孩子不要着急，妈妈会在这一直陪着你，直到你写完作业……”此刻我忽然感觉到，其实有母亲的陪伴就是我最大的幸福！ 还有一次，我和同学们一起在小区的花园里玩，正当我们玩游戏玩的不亦乐乎的时候，我被重重的摔了一跤，腿被摔破了。当时情不自禁的哭了起来，同学们听到我的哭声纷纷赶来，大家纷纷帮忙把我搀扶回家里。那时我又深深的感觉到，来自朋友的关心其实也是一种幸福。 记得那是一个风雨交加的下午，由于我的粗心来学的时候忘记了带雨伞。放学后我只能站在教室的窗口默默的看着外面的漂泊大雨……忽然，一个高大而熟悉的身影出现在我的眼前，忽然感觉双眼酸涩：“爸爸……”我一下子扑倒了爸爸的怀里，父亲的爱是无言的，这也正是我幸福的源泉！ 幸福就是有母亲的陪伴，朋友的关怀，父亲的关爱！每个人都向往幸福追求幸福，其实幸福就是一种感觉，是一种心态，它源于你对事与物的的追求与理解！ 感悟幸福作文2 著名作家海伦凯勒曾说过：“我一直在哭，一直在哭，哭我没有鞋穿，直到有一天，我知道有人没有脚…我由此知道了幸福的真正内涵。”幸福是什么？ 许多年前，当人们提出这个问题时，个个都两目相对，哑口无言。是啊，幸福是什么？幸福是那禾苗晃动稚嫩的身躯，昂首向天际发出的无声的诉说吗？幸福是蚕宝宝咀嚼着香脆的桑叶奏出的“沙沙沙沙”的乐曲吗？幸福是雏燕展翼穿过鹅黄的柳梢剪出的一片春色朝向母亲的回眸吗？幸福是梅花“已是

高考作文素材关于文明的作文素材

2015年高考作文素材：关于文明的作文素材 高考作文素材点拨及运用思路：文明 一、素材链接: 言语类 (一)名人名言 1.礼仪的目的与作用本在使得本来的顽梗变柔顺，使人们的气质变温和，使他尊重别人，和别人合得来。约翰?洛克 2.善气迎人，亲如弟兄;恶气迎人，害于戈兵。管仲 3.天下有大勇者，猝然临之而不惊，不故加之而不怒。苏轼 4.我们应该注意自己不用言语去伤害别的同志，但是，当别人用语言来伤害自己的时候，也应该受得起。刘少奇 5.礼貌使人类共处的金钥匙。松苏内吉 6.讲话气势汹汹，未必就是言之有理。萨迪 7.火气甚大，容易引起愤怒底烦扰，是一种恶习而使心灵向着那不正当的事情，那是一时冲动而没有理性的行动。彼得.阿柏拉德 8.青年人应当不伤人，应当把个人所得的给予各人，应当避免虚伪与欺骗，应当显得恳挚悦人，这样学着去行正直。夸美纽斯 9.礼貌是儿童与青年所应该特别小心地养成习惯的第一件大事。约翰.洛克 10.不论你是一个男子还是一个女人，待人温和宽大才配得上人的名称。一个人的真 11.正的英勇果断，决不等于用拳头制止别人发言。萨迪 12.礼貌使有礼貌的人喜悦，也使那些授人以礼貌相待的人们喜悦。孟德斯鸠 13.坏事情一学就会，早年沾染的恶习，从此以后就会在所有的行为和举动中显现出来，不论是说话或行动上的毛病，三岁至老，六十不改。克雷洛夫 14.礼貌经常可以替代最高贵的感情。梅里美 15.礼貌是最容易做到的事，也是最珍贵的东西。冈察尔 16.脾气暴躁是人类较为卑劣的天性之一，人要是发脾气就等于在人类进步的阶梯上倒退了一步。达尔文 17.蜜蜂从花中啜蜜，离开时营营的道谢。浮夸的蝴蝶却相信花是应该向他道谢的。泰戈尔

感受幸福作文650字

感受幸福作文650字 第1篇感受幸福作文650字 什么是幸福？幸福是各种各样的，不同的人对幸福的感受也各不相同。假如我们不会说话，那就不能表达自己内心的想法，不能向朋友倾诉自己的喜悦与烦恼，那是一种不幸，反之就是一种幸福。假如我们听不见，就不能感受到一切美妙的声音，如叮叮咚咚的琴声，哗哗的流水声，以及父母、老师、同学的说话声，那也是一种不幸，反之也是一种幸福。假如我们看不见，就不能欣赏到这个大千世界形形色色的样子，看不见斑斓的色彩，更看不见那美丽的太阳和月亮……这也是一种不幸，反之，，也是一种幸福。父母爱着我，就是一种幸福，别人帮助我，是一种幸福，能吃到美味的食物，也是一种幸福，快乐地生活着，更是一种幸福。被人信任是一种莫大的幸福。我平时有个坏习惯，不喜欢洗脸，一般都要妈妈督促着才勉强去洗。可有一次，妈妈回家比较晚。她到家时，我们都已经在吃晚饭了，正吃得津津有味时，妈妈回来了，她的第一句话就是“你今天有没有洗过脸？”，我赶紧回答：“我洗过了”。妈妈半信半疑：“你到底洗过没有？”，并盯着我，脸上的表情十分严肃。她这样做，其实是测试我有没有说谎。于是我更加坚定并大声地说：“我真的洗过了！”这次，她不再问了，并且笑了，我知道她相信了我的话，这时，我真的感受到了一种从未有过的幸福！其实，幸福还有很多很多，如爷爷奶奶的宠爱，父母亲的关怀，亲戚朋友们的赞美，老师们的期盼，同学们的友爱…。.都值得我们去好好珍惜，让我们常怀一颗感恩的心，时刻感受生活中的幸福吧！ 第2篇感受幸福作文650字 欧文曾说过,“人类的一切努力的目的在于获得幸福。幸福是什么？幸福是一种感觉，感动就是一种幸福。幸福就像作家毕淑敏说的：世上有预报台风的，有预报蝗虫的，有预报瘟疫的，有预报地震的，但没有人预报幸福，其实幸福和世界万物一样，有它的征兆。清晨，旭日东升，温柔的阳光洒向人间，抚摸着我的脸，心，感觉暖暖的。这，就是幸福的征兆。当我们在写作业时，一边写，一边贪婪地记下知识的结晶。这时，笔尖沙沙的响声，就是幸福的征兆。走在小河边，两岸的杨柳随风摆动，鱼儿和鹅卵石正在编制着充满活力的动态画面。这时，小河潺潺的流水声就是幸福的征兆。幸福的感觉是美好的，幸福的味道是甜美的。只不过它比玫瑰更芬芳，比棒棒糖更甜蜜，比巧克力更浓郁，比咖啡更香醇......幸福是什么？“幸福是贫困中相濡以沫的一块糕饼,患难中心心相印的一个眼神,父亲一次粗糙的抚摸,女友一个温馨的字条......这像一粒粒缀在旧绸子上的红宝石,在凄凉中愈发熠熠生辉。”幸福在哪里？当我们与家人一起散步时，幸福就在那温馨的眼神里；当我们和朋友们一起玩耍时，幸福就在那灿烂的笑容里；当我们与同学在一起探讨难题时，幸福就在那激烈的争辩里；当我们想象着自己的未来时，幸福就在那美好的憧憬里。幸福有时不一定是甜的，快乐有时也不一定就是幸福。快乐只是表面的开心，而幸福是发自内心的快乐和欣慰。因此，只要你是幸福的，那你一定是快乐的。让我们一起追求幸福吧！在生活中快乐成长，在成长中感受幸福。 第3篇感受幸福作文650字 幸福是什么？幸福对于每个人，都是不一样的！其实幸福可以很小，很简单，它时刻陪伴在我们的身边，也许是因为人们平时没有去在意，身边的那些本可以让人暖心的普通点滴，只要我们好好的去感受幸福！ 幸福就好比如这样：天悄悄拉下夜幕，同学们渐渐散了，不知什么时候，课室里只剩下我和她，她的画，也终于画好了。而我还是差一些才能搞定，她便开始收拾起来，时间更加紧迫，因此，我的心更加紧张，一滴晶莹的汗滴在了画纸上，我觉得实在画不下去了：小姨妈（外号），你觉得我的画还有有救吗！她凑过脑袋：来，我看看，当然有啦，快画吧！我看她望了望校门口，她一定是在找寻来接她回家的爸爸，我看看空空的课室：小姨妈，很晚