Multiphase level set with multi dynamic shape models on kidney segmentation of CT image

In this paper, a multiphase level set method with multi dynamic shape models is proposed to segment the kidneys on the abdominal computed tomography (CT) images. Comparing with the original Chan-Vese model three changes are made to improve the segmentation result. The first is using shape model to help the segmentation. The second is using dynamic shape model to deal with the variation of the kidneys. The third is using multi level set to simultaneously segment multi objects. We also develop an algorithm to automatically get the initial level set curves and initial shape models which are essential to apply the proposed method. In the experiments, the proposed method is compared with the Chan-Vese model and the single level set method with shape prior to prove that the proposed method can work better on the kidneys segmentation.

[1]  Théodore Papadopoulo,et al.  Efficient Segmentation of Piecewise Smooth Images , 2007, SSVM.

[2]  Yingjie Zhang,et al.  Edge Preserving Regularization for the Piecewise Smooth Mumford-Shah Model , 2007, EUROCON 2007 - The International Conference on "Computer as a Tool".

[3]  Lixu Gu,et al.  A novel shape prior based level set method for liver segmentation from MR Images , 2008, 2008 International Conference on Information Technology and Applications in Biomedicine.

[4]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[5]  Mubarak Shah,et al.  Modeling Interaction for Segmentation of Neighboring Structures , 2009, IEEE Transactions on Information Technology in Biomedicine.

[6]  Daniel Cremers,et al.  Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling , 2003, Scale-Space.

[7]  Hyeona Lim,et al.  A hybrid level set segmentation for medical imagery , 2005, IEEE Nuclear Science Symposium Conference Record, 2005.

[8]  Tony F. Chan,et al.  Level set based shape prior segmentation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Nahum Kiryati,et al.  Unlevel-Sets: Geometry and Prior-Based Segmentation , 2004, ECCV.

[10]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[11]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[12]  Xue-Cheng Tai,et al.  A binary level set model and some applications to Mumford-Shah image segmentation , 2006, IEEE Transactions on Image Processing.

[13]  Luminita A. Vese,et al.  Multiphase Object Detection and Image Segmentation , 2003 .

[14]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[15]  Peng Li,et al.  An Improved Approach to Image Segmentation Based on Mumford-Shah Model , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[16]  Li-jun Zhang,et al.  A Fast Image Segmentation Approach based on Level Set Method , 2006, 2006 8th international Conference on Signal Processing.