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| Graph Cuts and Shape Statistics Based Cardiac MR Image Segmentation Using Active Contours Model |
| LIU Fu-Chang1, ZHU Jin1, YANG Ya-Fang2, HENG Pheng Ann3, XIA De-Shen1 |
1.School of Computer Science and Technology, Nanjing University of Science and Technology,Nanjing 210094
2.The Second Affiliated Hospital, Nanjing Medical University, Nanjing 210011
3.Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong |
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Abstract To analyze heart function effectively, it is necessary to segment the left and right ventricles precisely. In cardiac MR images, the weak edges, broken boundaries, region inhomogeneity and noises cause difficulties in segmenting the contours of left and right ventricle precisely. In this paper, the training samples are aligned and analyzed, and the allowable shape space of the left and right ventricles is constructed. An active contours model based on graph cuts and shape statistics is proposed for segmentation of cardiac MR images. It uses graph cuts based active contours (GCBAC) to convert the image segmentation into the globally optimal partition after transforming the image into a graph. Next, GCBAC uses graph cuts to iteratively deform the contour. Consequently, it has a large capture range. Then, the shape statistics is introduced into GCBAC. The introduction of shape statistics prevents the deformation curve form leaking out of actual boundaries. Experimental results demonstrate the proposed method achieves a higher segmentation precision and a better stability than other approaches and it provides a feasible way for clinical applications.
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Received: 31 August 2007
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[1] Paragios N. A Variational Approach for the Segmentation of the Left Ventricle in MR Cardiac Image Analysis. International Journal of Computer Vision, 2002, 50(3): 345-362
[2] Chen Qiang, Zhou Zeming, Tang Min, et al. Shape Statistics Variational Approach for the Outer Contour Segmentation of Left Ventricle MR Images. IEEE Trans on Information Technology in Biomedicine, 2006, 10(3): 588-597
[3] Cremers D, Tischhuser F, et al. Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional. International Journal of Computer Vision, 2002, 50(3): 295-313
[4] Mitchell S C, Lelieveldt B P F, van der Geest R J, et al. Multistage Hybrid Active Appearance Model Matching: Segmentation of Left and Right Ventricles in Cardiac MR Images. IEEE Trans on Medical Imaging, 2001, 20(5): 415-423
[5] Xu Ning, Bansal R, Ahuja N. Object Segmentation Using Graph Cuts Based Active Contours // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Madison, USA, 2003, Ⅱ: 46-53
[6] Cootes T F, Taylor C J, Cooper D, et al. Active Shape Models—Their Training and Application. Computer Vision Image Understanding, 1995, 61(1): 38-59
[7] Ford L, Fulkerson D. Flow in Networks. Princeton, USA: Princeton University Press, 1962
[8] Boykov Y, Kolmogorov V. An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision. IEEE Trans on Pattern Analysis and Machine Intelligence, 2004, 26(9): 1124-1137
[9] Xu Chenyang, Prince J L. Snakes, Shapes, and Gradient Vector Flow. IEEE Trans on Image Processing, 1998: 7(3): 359-369
[10] Mikic I, Krucinski S, Thomas J D. Segmentation and Tracking in Echocardiographic Sequences: Active Contours Guided by Optical Flow Estimates. IEEE Trans on Medical Imaging, 1998, 17(2): 274- 283
[11] de Bruijne M, Nielsen M. Shape Particle Filtering for Image Segmentation // Proc of the 7th International Conference on Medical Image Computing and Intervention. Rennes, France, 2004: 168-175
[12] Lelieveldt B P F, zümcü M, van der Geest R J, et al. Multi-View Active Appearance Models for Consistent Segmentation of Multiple Standard Views: Application to Long- and Short-Axis Cardiac MR Images // Proc of the 17th International Congress and Exhibition on Computer Assisted Radiology and Survey. London, UK, 2003: 1141-1146
[13] Stegmann M B, Fisker R, Ersbll B K. Extending and Applying Active Appearance Models for Automated, High Precision Segmentation in Different Image Modalities // Proc of the 12th Scandinavian Conference on Image Analysis. Bergen, Norway, 2001: 90-97 |
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2009, Vol. 22 
