A Framework for Auto-segmentation of Left Ventricle from Magnetic Resonance Images

Transcription

1 APCOM & ISCM th Deceber, 2013, Singapore A Fraewor for Auto-egentation of Left Ventricle fro Magnetic Reonance Iage *Xulei Yang 1, Si Yong Yeo 1, Calvin Li 1, Yi Su 1, Min Wan 2, Liang Zhong 2, and Ru San Tan 2 1 Coputing Science Departent, Intitute of High Perforance Coputing, A*STAR, 1 Fuionopoli Way #16-16 Connexi, Singapore National Heart Centre Singapore, 17 Third Hopital Avenue, Mitri Wing, Singapore *Correponding author: yangx@ihpc.a-tar.edu.g Abtract A practical fraewor i propoed for the auto-egentation of the left ventricle (LV endocardiu boundary in cardiac agnetic reonance (CMR iage. The egentation ethod i baed on the rando wal (RW algorith, which require uer-elected bacground and foreground eed. In thi paper, the eed are initialized autoatically. The firt iage frae of a hort-axi lice i firt partitioned into different region uing the fuzzy clutering algorith, and the LV region i identified uing a heuritic ethod. Two circular region of interet (ROI are then defined baed on the etiated centre of the partitioned LV region, which are ued a the RW eed initialization to egent the LV of the firt iage frae. The centre pixel of the adjacent iage frae i then coputed uing the egented LV of the previou frae. The foreground and bacground circular ROI can then be defined and ued a initialization of the RW algorith to egent the adjacent iage. The effectivene of the propoed fraewor i verified by the experiental reult on real CMR iage. Keyword: Cardiac Magnetic Reonance, Iage Segentation, Rando Wal, Left Ventricle, Fuzzy Clutering. 1. Introduction Cardiac iage egentation play a crucial role and allow for a wide range of application, including quantification of volue, coputer-aided diagnoi, localization of pathology, and iage-guided intervention. However, anual delineation i tediou, tie-conuing, and i liited by inter- and intra- oberver variability. In addition, any egentation algorith are enitive to the initialization and therefore the reult are not alway reproducible, which i alo liited by inter algorith variability. Furtherore, the aount and quality of iaging data that need to be routinely acquired in one or ore ubject ha increaed ignificantly. Therefore, it i crucial to develop autoated, precie, and reproducible egentation ethod. Cardiac Magnetic Reonance (CMR i a well etablihed and rapidly advancing iaging odality in analyzing heart dieae. It i conidered by oe author to be the reference tandard. CMR ha proved to be ore accurate than echo-cardiology in the calculation of the ejection fraction and alo hown uperior reult in endo-cardiu border egentation. It ha a wide topographical field of view and high contrat between oft tiue without the need for a contrat agent. Thi ean there i a high dicriination between the flowing blood and the yocardiu ucle. It i non-invaive with high patial reolution and can be gated uing an elec-trocardiogra (ECG at different phae during the heart pule. In thi tudy, the propoed fraewor ai to egent the left ventricle (LV endo-cardiu border in CMR iage. Segentation and tracing of LV in CMR data ha been extenively addreed for the lat decade (Petitjean, 2011; Kang, Baically, the LV egentation ta can be approached by four ajor approache: iage-baed ethod (e.g., Weng, 1997, deforable odel-baed (e.g., Jolly, 1

2 2006 ethod, regitration-baed ethod (e.g., Lorenzo-Valdé, 2004, and graph-baed ethod (e.g., Kedenburg, 2006: The firt approach utilize the baic iage analyi operator lie threholding, region-growing, iage orphology, edge detection, pixel claification, etc, to delineate the LV boundarie fro the iage. The econd approach train a hape/curve odel of the LV, and let the curve odel evolve in new ubject until it converge to the LV boundarie. The baic idea of the third approach i to tranfer thoe expert-egentation in training iage (i.e., atlae onto target iage through iage regitration, and then fue the tranferred egentation to derive an ultiate egentation. The lat approach ha alo been eployed in LV egentation without heavy reliance on explicitly learned or encoded prior, but the uer ha to initialize the et of foreground and bacground eed. Graph-baed ethod have been uccefully eployed in iage egentation without heavy reliance on explicitly learned or encoded prior. However, graph cut algorith propoed in (Boyov, 2000 i a fundaentally two-label algorith, and uceptible to the all cut proble in the preence of wea boundarie. The rando wal (RW algorith propoed in (Grady, 2004 & 2006 doe not uffer fro the all cut proble and extend naturally to an arbitrary nuber of label. A the good perforance of wea boundary detection, noie robutne, and the aignent of abiguou region, RW egentation ha been applied in cardiac data, MR brain iage (Grady, 2004 & 2006, Elai, 2013, Daua, However, anual election of the eed i a hard ta in low intenity varying edical iage, and liit it application for real proble. In thi tudy, a new fraewor i propoed for the auto-egentation of the LV endo-cardiu boundary in CMR iage. The egentation ethod i baed on the RW algorith, and the eed are initialized autoatically. The firt iage frae of a hort-axi lice i firt partitioned into different region uing the fuzzy c-ean (FCM algorith (Bezde, 1981, and the LV region i identified uing a heuritic ethod. Two circular region of interet (ROI are then defined baed on the etiated centre of the partitioned LV region, which are ued a the RW eed initialization to egent the LV of the firt iage frae. The centre pixel of the adjacent iage frae i then coputed uing the egented LV of the previou frae. The foreground and bacground circular ROI can then be defined and ued a initialization of the RW algorith to egent the adjacent iage. The effectivene of the propoed fraewor i verified by the experiental reult on real CMR iage fro RV challenge webite ( The ret of thi paper i organized a follow: Section 2 review the related technique and preent the propoed fraewor. The experiental reult of the propoed fraewor are reported in Section 3. Finally, the concluion i given in Section Methodology In thi ection, we firt brief review the FCM and RW algorith, then preent the propoed cardiac iage egentation fraewor, which i pecifically for LV endo-cardiu boundary delineation in CMR iage. 2.1 Fuzzy C-Mean (FCM Matheatically, FCM algorith i forulated to iniize the following objective function with repect to the eberhip function u and the cluter centre v a given by J fc = c x I = 1 2 ( u ( x v I ubject to u = 1, x (1 2 c = 1

3 Where c i the cluter nuber, i the weighing exponent on fuzzy eberhip, and a value of = 2 i nown to give good reult with the FCM algorith. Note if = 1, then the FCM becoe the hard K-ean algorith (Macqueen, 1967 with each point uniquely belonging to it nearet cluter. The u i the eberhip of the point x in the th cluter. The iniization of (1 give the updating equation for eberhip u and cluter centre v, which are given by u ( x = K = 1 v ( x v 2 /( 1 2 /( 1 v = x I and (2 x I u u x The FCM algorith i uarized a follow, 1 Fix the cluter nuber c, initialize the cluter centre v (=1,2, c, and et the threhold ε be a all poitive value, e.g., ε= Alternatively update the eberhip function and cluter centre by uing (2 until the change between two iteration aller than the threhold ε 2.2 Rando Wal (RW RW algorith wa firt propoed by Leo Grady (2004 & 2006 for perforing ulti-label, interactive iage egentation. Uer predefine a erie of pixel a the label, then the probability that a rando waler tarting at each unlabeled pixel (a pixel can be conidered a a node will firt reach one of the prelabeled pixel on different label i calculated. A high-quality egentation iage can be obtained by aigning to each node the label correponding to the greatet probability. The uary of the RW algorith i decribed a follow: 1 Obtain a et of ared (or labeled pixel with K label (wher K i the nuber of the label, either interactively or autoatically. 2 Build the lattice, which i copoed of node and edge. RW treat an iage a a purely dicrete object -- a graph with a fixed nuber of vertice and edge. 3 Chooe a weighting function, which ap a change in iage intenitie to edge weight. The typical Gauian weighting function i given by w = exp( β ( g i g j 2 (3 Where g i indicate the iage intenity at pixel i, and β i the weighting paraeter. 4 Solve each label by L U x = B T (4 Where x (0 < K repreent a vector of probabilitie for each node to reach to the eed with label. Pleae refer to (Grady, 2006 for the definition of L U, B T and. 5 Aign to each node the label according to the axiu probability ax ( x i, and the final egentation can be obtained. The detailed dicuion of RW algorith for iage egentation can be found in (Grady, 2004 & It i noted that the eed election i the ey tep for RW ipleentation. Segentation reult i extreely enitive to the poition of eed, and any iproper poition of eed will lead to the fale reult. 3

4 2.3 The propoed fraewor Thi tudy ai to propoe a fully autoated fraewor for cardiac iage egentation with the auto initialization of the bacground and foreground eed for RW algorith. The FCM algorith and heuritic ethod are ued to locate the LV region in the firt iage frae of a hort-axi lice, then the eed are initialized baed on the centre of the located LV region, after that, the RW algorith i ued to egent the firt frae. In equence, the centre of the egented LV region of the current frae i then propagated to the adjacent frae to initialize the eed and RW i ued to egent the LV boundary in the frae. Fig.1 Illutration of the propoed fraewor for egentation of a equence of CMR iage, refer to the ain text for the detail. The propoed fraewor for the LV egentation on a equence of frae can be uarized a follow, 1 Given the firt frae in Fig.1-a, et cluter nuber c=3, uing the FCM algorith to partition the firt frae into different region with three intenity level, low, iddle and high, a hown in Fig.1-b. 2 Aong the region with high intenity level, chooe two big region whoe centre have iilar y coordinate, and identify LV a the right one (the left one i right ventricle (RV, a hown in Fig.1-c. The LV region i indicated by red-colour rectangle. 3 Calculate the centre of LV region, and initialize foreground and bacground eed for the firt frae, a hown in Fig.1-d. The green-colour and blue-colour dot indicate the foreground and bacground eed, repectively. 4 Segent the firt frae by uing RW algorith with the initialized eed fro tep 3, the egented LV boundary i hown in Fig.1-e. The Segented LV boundary i indicated by redcolour curve. 5 Calculate the centre of LV boundary, propagate it to the adjacent frae, and initialize foreground and bacground eed for the adjacent frae, a hown in Fig.1-f. The green-colour and blue-colour dot indicate the foreground and bacground eed, repectively. 4

5 6 Segent the adjacent frae by uing RW algorith with the initialized eed fro tep 5, the egented LV boundary i hown in Fig.1-g. The Segented LV boundary i indicated by redcolour curve. 7 Repeat tep 5 and 6 to egent the next adjacent frae until it i done on all the frae in the equence, a hown in Fig.1-h. The location of LV region in the firt frae in tep 2 i baed on the LV and RV intenity ditribution and their overall relevant location in the CMR iage. 3. Experientation To evaluate the perforance of the propoed fraewor on cardiac LV egentation, we apply it to the real CMR iage fro RV challenge webite ( In all exaple, we fix the cluter nuber c=3 for FCM clutering algorith and the weighting paraeter β=90 for RW egentation algorith. The oftware ued in thi tudy i odified baed on the MATLAB ource code fro L. Grady hoepage ( In all exaple, the green dot indicate the foreground eed, blue dot indicate the bacground eed, and red curve indicate the egented LV boundarie. Fig.2 The eed initialization for the firt frae, i.e., frae #100. Fig.3 The egentation reult of the propoed fraewor on the frae #100 to #109 (only how the frae with even nuber. Here the nuber of bacground eed and foreground eed are 8 and 4, repectively. 5

6 Fig.4 The egentation reult of the propoed fraewor on the frae #100 to #109 (only how the frae with even nuber. Firt row, the nuber of bacground eed and foreground eed are reduced to 4 and 2, repectively. Second row, the nuber of bacground eed and foreground eed are reduced to 2 and 2, repectively. Fig.5 The egentation reult of different eed location on the ae original iage. Note the location of the bacground and foreground eed are changed, which affect the egentation reult. The iage ize i 256x216, 20 iage per cardiac cycle. The teting i perfored on the patient #1, frae #100 to frae #119 (one cardiac cycle. We elect frae #100 a the firt frae, and locate the LV region by uing FCM and heuritic ethod, a hown in Fig.2. The egented reult of the firt frae i then propagated to initialize the eed and egent all the frae in the equence, a hown in Fig.3. It can be oberved fro the figure that the egented LV boundarie are quite reaonable even though papillary ucle are adjacent to or fall inide the LV region in oe frae. We need to highlight that whole egentation procedure i fully autoatic proceed, which i deirable for practical clinic application. It i noted that the egentation reult are not enitive to the nuber of the eed, if we reduce the nuber of bacground and foreground eed fro 8 and 4 to 4 and 2, or even 2 and 2, repectively, the egentation reult are quite iilar, a hown in Fig.4. However, the location of the bacground and foreground eed ay affect the egentation reult, a hown in Fig.5, the egentation ay fail if the eed are not properly located. 6

7 4. Concluion Accurate and robut extraction of the LV cavity i the ey tep for analyzing heart function quantitatively. In thi tudy, we propoe a fraewor for fully autoated egentation of the LV boundary in CMR iage in ter of fuzzy clutering and graph egentation technique. The effectivene of the propoed fraewor ha been verified by the experiental reult of LV egentation on real CMR iage. Thi tudy focue on LV endocardial delineation only, to extend the propoed fraewor for LV epicardial delineation i one of the future reearch topic. Reference Petitjean, C. and Dacher, J.N. (2011, A review of egentation ethod in hort axi cardiac MR iage, Medical Iage Analyi, 15, pp Kang, D., Woo, J., Sloa, P.J., Dey, D., Gerano, G. and Jay-Kuo, C. (2012, Heart chaber and whole heart egentation technique: review, SPIE Journal of Electronic Iaging, 21, pp Weng, J., Singh, A., and Chiu, M. (1997. Learning-baed ventricle detection fro cardiac MR and CT iage. IEEE Tran. on Medical Iaging, 16, pp Jolly, M.-P. (2006, Autoatic egentation of the left ventricle in cardiac MR and CT iage. International Journal of Coputer Viion, 70, pp Lorenzo-Valdé, M., Sanchez-Ortiz, G., Elington, A., Mohiaddin, R., and Ruecert, D. (2004. Segentation of 4D cardiac MR iage uing a probabilitic atla and the EM algorith. Medical Iage Analyi. 8, pp Kedenburg, G., Cococo, C., Kothe, U., Nieen, W., Vonen, E., and Viergever, M. (2006. Autoatic cardiac MRI yocardiu egentation uing graphcut. Proceeding of SPIE, Medical Iaging, Boyov, Y. and Jolly, M. P. (2000, Interactive Organ Segentation Uing Graph Cut, in Medical Iage Coputing and Coputer-Aited Intervention, pp Grady, L. and Funa-Lea, G. (2004, Multi-Label Iage Segentation for Medical Application Baed on Graph- Theoretic Electrical Potential, ECCV04, pp Grady, L. (2006, Rando Wal for Iage Segentation, IEEE Tran. on Pattern Analyi and Machine Intelligence, 28, pp Elai, A., Karaali, A., Katouzian, A. and Navab, N. (2013, Segentation by retrieval with guided rando wal: application to left ventricle egentation in MRI. Medical Iage Analyi, 17, pp Daua, S.P. and Sahabi, J.S. (2011, Modified active contour odel and rando wal approach for left ventricular cardiac MR iage egentation, International Journal for Nuerical Method in Bioedical Engineering, 27, pp Bezde, J.C. (1981, Pattern Recognition with Fuzzy Objective Function Algorith, Plenu Pre, New Yor. Macqueen, J.B. (1967, Soe ethod for claification and analyi of ultivariate obervation, proceeding of 5 th Berley Sypoiu on Matheatical Statitic and Probability, pp