The domain knowledge based graph-cut model for liver CT segmentation

Abstract In this paper, we present a semi-supervised approach for liver segmentation from computed tomography (CT) scans, which is based on the graph cut model integrated with domain knowledge. Firstly, some hard constraints are obtained according to the knowledge of liver characteristic appearance and anatomical location. Secondly, the energy function is constructed via knowledge based similarity measure. A path-based spatial connectivity measure is applied for robust regional properties. Finally, the image is interpreted as a graph, afterwards the segmentation problem is casted as an optimal cut on it, which can be computed through the existing max-flow algorithm. The model is evaluated on MICCAI 2007 liver segmentation challenge datasets and some other CT volumes from the hospital. The experimental results show its effectiveness and efficiency.

[1]  Elena Casiraghi,et al.  Liver segmentation from computed tomography scans: A survey and a new algorithm , 2009, Artif. Intell. Medicine.

[2]  J. Dillenseger,et al.  Graph Cut Liver Segmentation for Interstitial Ultrasound Therapy , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  S. Casciaro,et al.  Fully Automatic Liver Segmentation through Graph-Cut Technique , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  D. R. Fulkerson,et al.  Flows in Networks. , 1964 .

[5]  Ioannis A. Kakadiaris,et al.  A shape-driven MRF model for the segmentation of organs in medical images , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Hans-Peter Meinzer,et al.  A Shape-Guided Deformable Model with Evolutionary Algorithm Initialization for 3D Soft Tissue Segmentation , 2007, IPMI.

[7]  Ioannis A. Kakadiaris,et al.  Fuzzy-Cuts: A knowledge-driven graph-based method for medical image segmentation , 2009, CVPR.

[8]  Nikos Paragios,et al.  Liver Segmentation Using Sparse 3D Prior Models with Optimal Data Support , 2007, IPMI.

[9]  Ning Xu,et al.  Object segmentation using graph cuts based active contours , 2007, Comput. Vis. Image Underst..

[10]  Martin Styner,et al.  Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets , 2009, IEEE Transactions on Medical Imaging.

[11]  Xing Zhang,et al.  Automatic Liver Segmentation Using a Statistical Shape Model With Optimal Surface Detection , 2010, IEEE Transactions on Biomedical Engineering.

[12]  Dorin Comaniciu,et al.  Hierarchical, learning-based automatic liver segmentation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  H. Lamecker,et al.  Segmentation of the Liver using a 3D Statistical Shape Model , 2004 .

[14]  Thomas Lange,et al.  Shape Constrained Automatic Segmentation of the Liver based on a Heuristic Intensity Model , 2007 .

[15]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[16]  Zhao Weidong,et al.  Level Set Segmentation Algorithm Based on Image Entropy and Simulated Annealing , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[17]  Vladimir Kolmogorov,et al.  An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision , 2004, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Anton Osokin,et al.  Fast Approximate Energy Minimization with Label Costs , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[19]  Wang Nian.  An Images Matching Algorithm Based on Graph Cuts , 2006 .

[20]  Gareth Funka-Lea,et al.  Graph Cuts and Efficient N-D Image Segmentation , 2006, International Journal of Computer Vision.