Accurate segmentation of the myocardium in cardiac magnetic resonance images can be restricted by image noise and low discrimination between the epi-cardium boundary and other organs. Segmentation of the epi-cardium is important for the calculation of left ventricle mass. In this paper we propose a novel method of epi-cardium segmentation, which firstly segments the left ventricle cavity. The epi-cardium boundary is found using the edge information in the image, and where such information is lacking it enhances the shape with the best fitting scaled segment, taken from a database of expertly assisted hand segmented images. In the final stage the segments are connected using a natural closed spline. The method was evaluated using a leave-one-out strategy on 24 volumes and calculates the coefficient of determination as 0.93 and a root mean square of the point to curve error of 1.54 mm when compared to manually segmented images

Ghita, Ovidiu

Lynch, Michael

Whelan, Paul F.

English

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Extraction of epi-cardium contours from unseen images using a shape database

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Extraction of Epi-cardium contours from unseenimages using a shape databaseMichael LynchVision Systems GroupDublin City UniversityDublin 9, IrelandEmail: lynchm@eeng.dcu.ieTelephone : +353-1-700-7637Ovidiu GhitaVision Systems GroupDublin City UniversityDublin 9, IrelandPaul F. WhelanVision Systems GroupDublin City UniversityDublin 9, IrelandAbstract— Accurate segmentation of the myocardium in car-diac magnetic resonance images can be restricted by image noiseand low discrimination between the epi-cardium boundary andother organs. Segmentation of the epi-cardium is important forthe calculation of left ventricle mass.In this paper we propose a novel method of epi-cardiumsegmentation which firstly segments the left ventricle cavity. Theepi-cardium boundary is found using the edge information inthe image, and where such information is lacking it enhances theshape with the best fitting scaled segment, taken from a databaseof expertly assisted hand segmented images. In the final stage thesegments are connected using a natural closed spline.The method was evaluated using a leave-one-out strategy on 24volumes and calculates the coefficient of determination as 0.93and a root mean square of the point to curve error of 1.54mmwhen compared to manually segmented images.I. INTRODUCTIONCardiac Magnetic Resonance (CMR) has become animportant tool for the prognosis and treatment monitoringof cardiovascular disease (CVD). CMR is preferred due toits short acquisition time, < 10 minutes for ECG-gated cineCMR or < 10 seconds for the newest scanners where the cinestack can be obtained in a single breath hold [1]. In additionto the fast acquisition time, the patient is not exposed to highdegrees of radiation as is the case in Computer Tomography(CT).CMR is considered by some to be the reference standardfor the measurement of the left ventricle volume (LVV)and left ventricle mass (LVM) [2], [3]. The left ventriclevolume is used in the calculation of the ejection fraction (EF),which is a standard medical indicator of a hearts state ofhealth. The LVM can flag an enlargement of the ventricularmuscle (hypertrophy) and can a predicator for increased riskof cardiovascular mortality and morbility [1]. The positionand extent of systolic wall thickening are also useful in theassessment of ischemic heart disease.The increased volume of data generated by the latest CMRscanners presents a vast amount of high resolution volumetricdata to the specialist. Interpreting and analyzing this largeamount of data represents a tedious and time-consuming taskfor the cardiologist. Manual delinearisation is still a prominentmethod used to segment the left ventricle myocardium. Sincemanual segmentation is labour-intensive, time-consumingand suffers from inter- and intra-observer variability, theprospect of an automatic and accurate segmentation is highlydesirable. Automatic segmentation will therefore enhancecomparability between and within cardiac studies and increaseaccurate evaluation of volumes by allowing acquisition ofthinner MRI-slices. For this reason segmentation of the leftventricle from CMR images is necessary for finding all thesemeasurements and therefore is an area of on-going research[4], [5].Semi-automated methods have being developed whichusually requires a lot of interaction and intervention by theuser. This reduces the time involved in the segmentationbut still suffers from inter- and intra-observer variability.Low-level operations such as thresholding have also beinginvestigated [6].Much literature has being written on model basedsegmentation approaches [4]. Known problems associatedwith model based approaches are their failure or attractionto erroneous points in areas of low contrast like those foundbetween the epi-cardium and the lungs. The accuracy of thesegmentation relies heavily on the amount and variation ofimages in the training set. If the training set is too smallwith small variation, there is a limited number of unseenimages that the model is applicable too. If, on the other hand,the model is large with large variation it may choose someerroneous points. Convergence is defined by the step-to-stepchanges in shape, scale, rotation, and position falling belowa tolerance value.A method is proposed which uses the low level informationin the image where it is available. Where such information itis not available it uses a model that has being built up frompreviously hand-segmented images. The finished segmentationcontains actual information obtained from the image andappends it with a priori knowledge where necessary.0-7803-8701-5/04/$20.00 (C) 2004 IEEE0-7803-8700-7/04/$20.00 (C) 2004 IEEE 3685Authorized licensed use limited to: DUBLIN CITY UNIVERSITY. Downloaded on July 27,2010 at 11:13:07 UTC from IEEE Xplore.  Restrictions apply. Database of pre-segmented heart muscles(i)(ii) (iii)(iv)(vii)(vi)(v)(viii)Fig. 1. Shows the segmentation scheme from the original image (i) to thecomplete segmented epi-cardium boundary (viii)This paper is presented as follows; section 2 describesthe methods involved in the segmentation, firstly a briefdescription of how the left ventricle cavity is obtained. Wethen move on to explain how the valid segments from theepi-cardium are found and how these segments are usedto obtain the best fitting model from the database. Finally,results are presented for the segmentation against manuallysegmented images with some final conclusions.II. METHODThe search for the epi-cardium border starts with thesegmentation of the left-ventricle cavity. This is achievedby smoothing the 2D slices using an diffusion-based filter[7]. The use of nonlinear diffusion-based filters has beeninvestigated for use in medical images and in particularCMR. In their formulation, the blurring would be performedwithin homogeneous image regions with no interactionbetween adjacent or neighbouring regions that share acommon border. The non-linear diffusion procedure canbe written in terms of the derivative of the flux function,φ(∇I) = ∇I · D(‖∇I‖), where φ is the flux function, I isthe image and D is the diffusion function. This equation isimplemented in an iterative manner to obtain improved results.Segmentation of the left ventricle cavity is achieved byclustering the image, see figure 1 (ii). The clustering isperformed on the 3D volume using an unsupervised k-meansclustering technique [8], which classifies pixels into groupsor clusters using a distance criteria to join data values to eachcluster. The process then moves the elements between clusterswith two aims,(i) to minimise the variation within each cluster and(i) to maximise the variation between clustersThe process terminates when no more elements are exchangedbetween clusters and is convergent.Once the endo-cardium border has been successfullydelineated, the second step is to find the epi-cardium border.The algorithm is a 2D approach working on the individualslices from the volume. Once each slice is taken from thevolume the centre of gravity of the left ventricle blood poolis located and the least squares approximation for the radiusof the endo-cardium border is calculated. By re-clustering theimage again around a smaller region of interest with a setlow number of clusters in order to find the right ventricleblood pool. The right ventricle blood pool is found to bethe largest cluster, close to the left ventricle cavity withsimilar attributes to the left ventricle blood pool. Once theright ventricle blood pool has being located, the myocardiumwall (interventricular septum) between the two ventricles ismeasured. This measurement gives an approximate thicknessfor the myocardium around the left ventricle.A non-maximum suppression edge-detector is used on theoriginal image slice, see figure 1 (iii). A radial search isperformed of the gradient image from the centre of gravityof the left ventricle cavity to determine possible epi-cardiumedges. From this gradient image, edge points are connectedtogether into edge segments using a Euclidean distancecriterion. Spurious segments are eliminated by length, byorientation away from the endo-cardium border and using theapproximation for the myocardium from the interventricularseptum. The remaining segments are shown in figure 1 (iv).Between the segments are gaps where there is no distin-guishable gradient between the myocardium and the otherorgans. Therefore there is no other information in the image tohelp find the correct path between these segments. In this casea priori knowledge about the shape obtained from previouslyhand-segmented can be used to join the segments. In this waywe introduce a type of supervision, and by inferring expertlydrawn contours we maintain continuity of the shape. Becausethe contour maintains its original segments, the manually0-7803-8701-5/04/$20.00 (C) 2004 IEEE0-7803-8700-7/04/$20.00 (C) 2004 IEEE 3686Authorized licensed use limited to: DUBLIN CITY UNIVERSITY. Downloaded on July 27,2010 at 11:13:07 UTC from IEEE Xplore.  Restrictions apply. drawn contours are only inferred where there is no informationto be found in the image. We believe that this is a moreappropriate solution as when complete models are inferredonto the image there is a danger that details may be lost.(a) (b)(c) (d)Fig. 2. Figure shows four images, (a) Original unseen image, (b) calculatededges, (c) robust segments of epi-cardium and (d) the complete segmentationAppropriate shapes are selected from the database usingthe radius least mean squares approximation for both theendo-cardium and the epi-cardium segments. The database issearched for shapes that fall within a threshold 10% of theseradii. The shapes are then translated to the correct spatialposition using the centre of gravity of the left ventricle cavity,see figure 1 (vi).Once the list of appropriate shapes has been extracted fromthe database the next step is to close the segments of the epi-cardium shape using the best fitting database entry. For everytwo segments, the deviation is calculated for each of the shapesin the database list. The shape with the minimum deviation ischosen and the appropriate points from the database shape areused to fill the between the segments. This is completed forevery two segments and the first and final segments. The pointson the epi-cardium shape are then joined using a natural cubicclosed spline of order three, see figure 1 (viii).III. RESULTSAn evaluation of the results was made using 24 volumeimages taken from patient transversing the left ventricle fromthe base to the apex along the short axis. The imaging devicewas a Siemens Magnetom Sonata 1.5 Tesla, TR = 3.2,TE = 1.6, cine acquisition with TrueFISP balancing at aresolution of (1.37x1.37x 8mm).The volumes were assessed using a leave-one-out strategy,with the model containing the information for 23 volumes andevaluation performed on the unseen volume. Figure 3(a) showsthe Bland-Altmann plot which indicates good reproducibilitywith this method, secondly a linear graph, figure 3(b) isplotted and gives an r2 of 0.93.The point to curve measurement is the Euclidean distancefrom each point in the automatically delineated boundary tothe closest point on the manual delineated boundary. The valueof the root mean square of the standard deviation for 150 sliceswas 1.54mm.IV. CONCLUSIONThis paper describes a novel approach for the delinerisationof the epi-cardial boundary of the left ventricle. The algorithmuses all the available information within the images, butwhere there is no useful information present the algorithmsearches a database for appropriate segments.Due to the low contrast in some areas of the images thatseparate the epi-cardium from other organs in the body, agood correlation between segmentations is extremely hardto accomplish even between expert tracings. Inter- andIntra-observer variability is very high for the delineationof the epi-cardial boundary and knowledge and expertiseof the cardiologist is a major factor. This is one of themain reasons for implementing a database model fromhand-segmented images. Other deformable model approachesaim find strong gradient features of the epi-cardial boundaryand from this information find the model instance that fitswith the least amount of error. However the model-assistedalgorithm presented here enhances the strong features of theepi-cardial boundary already found in the image with themost appropriate segment from the hand-annotated images toreduce the error while maintaining continuity of the curve.This method presents a robust segmentation of the epi-cardialboundary even in the presence of low contrast between themyocardium and other organs.REFERENCES[1] S. G. Myerson, N. G. Bellenger, and D. J. Pennell, “Assessment of leftventricular mass by cardiovascular magnetic resonance,” Hypertension,vol. 39, pp. 750–755, 2002.[2] M. Dulce and G. Mostbeck, “Quantification of the Left VentricularVolumes and Function with Cine MR Imaging: Comparison of GeometricModels with Three-Dimensional Data,” Radiology, vol. 188, no. 371-376,1993.[3] S. Schalla, E. Nagel, H. Lehmkuhl, C. Klein, A. Bornstedt, B. Schnack-enburg, U. Schneider, and E. Fleck, “Comparison of Magnetic ResonanceReal-Time Imaging of Left Ventricular Function with Conventional Mag-netic Resonance Imaging and Echocardiography,” The American Journalof Cardiology, vol. 87, no. 1, pp. 95–99, 2001.[4] F. Frangi et al., “Three-Dimensional Modelling for Functional Analysisof Cardiac Images: A Review,” IEEE Transations on Medical Imaging,vol. 20, no. 1, 2001.[5] R. J. van der Geest and J. Reiber, Cardiovascular MRI and MRA.Philadelphia: Lippincott,Williams and Wilkins, 2003, ch. 5.0-7803-8701-5/04/$20.00 (C) 2004 IEEE0-7803-8700-7/04/$20.00 (C) 2004 IEEE 3687Authorized licensed use limited to: DUBLIN CITY UNIVERSITY. Downloaded on July 27,2010 at 11:13:07 UTC from IEEE Xplore.  Restrictions apply. -400-300-200-1000100200300400500750 1250 1750 2250 2750Difference of manual and automatic mm2Average of manual and automatic mm2meanmean + 2SDmean - 2SD(a)50010001500200025003000500 1000 1500 2000 2500 3000Manual Area mmAutomatic Area mm22(b)Fig. 3. Figures (a)-(b) show the Bland-Altman and the scatterline plot of manual segmentation against the automatic segmentation for the epi-cardium areas.[6] J. Suri, “Computer Vision, Pattern Recognition and Image Processing inLeft Ventricle Segmentation: The Last 50 Years,” Pattern Analysis andApplications, vol. 3, no. 3 2000, pp. 209–242, 2000.[7] P. Perona and J. Malik, “Scale-space and edge detection using anisotropicdiffusion,” IEEE Trans. on Pattern Analysis and Machine Intelligence,vol. 12, no. 7, pp. 629–639, 1990.[8] R. Duda and P. Hart, Pattern Classification and Scene Analysis. NY:Wiley, 1973.0-7803-8701-5/04/$20.00 (C) 2004 IEEE0-7803-8700-7/04/$20.00 (C) 2004 IEEE 3688Authorized licensed use limited to: DUBLIN CITY UNIVERSITY. Downloaded on July 27,2010 at 11:13:07 UTC from IEEE Xplore.  Restrictions apply. 

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Assessment of left ventricular mass by cardiovascular magnetic resonance,”

Comparison of Magnetic Resonance Real-Time Imaging of Left Ventricular Function with Conventional Magnetic Resonance Imaging and Echocardiography,”

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Quantiﬁcation of the Left Ventricular Volumes and Function

Three-Dimensional Modelling for Functional Analysis of Cardiac Images: A Review,”

Extraction of epi-cardium contours from unseen images using a shape database

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