Liver Segmentation Approach Using Graph Cuts and Iteratively Estimated Shape and Intensity Constrains

In this paper, we present a liver segmentation approach. In which, the relation between neighboring slices in CT images is utilized to estimate shape and statistical information of the liver. This information is then integrated with the graph cuts algorithm to segment the liver in each CT slice. This approach does not require prior models construction, and it uses single phase CT images; even so, it is talented to deal with complex shape and intensity variations. Moreover, it eliminates the burdens associated with model construction like data collection, manual segmentation, registration, and landmark correspondence. In contrast, it requires a low user interaction to determine the liver landmarks on a single CT slice only. The proposed approach has been evaluated on 10 CT images with several liver abnormalities, including tumors and cysts, and it achieved high average scores of 81.7 using MICCAI-2007 Grand Challenge scoring system. Compared to contemporary approaches, our approach requires significantly less interaction and processing time.

[1]  Yen-Wei Chen,et al.  Automated segmentation of the liver from 3D CT images using probabilistic atlas and multilevel statistical shape model. , 2008, Academic radiology.

[2]  Marko Subasic,et al.  Level Set Methods and Fast Marching Methods , 2003 .

[3]  Vipin Chaudhary,et al.  Segmentation of the Liver from Abdominal CT Using Markov Random Field Model and GVF Snakes , 2008, 2008 International Conference on Complex, Intelligent and Software Intensive Systems.

[4]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..

[5]  Nassir Navab,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010, 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part III , 2010, MICCAI.

[6]  Yeong-Gil Shin,et al.  Efficient liver segmentation using a level-set method with optimal detection of the initial liver boundary from level-set speed images , 2007, Comput. Methods Programs Biomed..

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

[8]  Norimichi Tsumura,et al.  A Model Optimization Approach to the Automatic Segmentation of Medical Images , 2010, IEICE Trans. Inf. Syst..

[9]  László Ruskó,et al.  Automatic segmentation of the liver from multi- and single-phase contrast-enhanced CT images , 2009, Medical Image Anal..

[10]  Gerardo Tibamoso,et al.  Semi-automatic Liver Segmentation From Computed Tomography ( CT ) Scans based on Deformable Surfaces , 2009 .

[11]  Yoshinobu Sato,et al.  Liver segmentation by intensity analysis and anatomical information in multi-slice CT images , 2009, International Journal of Computer Assisted Radiology and Surgery.

[12]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[13]  Max A. Viergever,et al.  Efficient and reliable schemes for nonlinear diffusion filtering , 1998, IEEE Trans. Image Process..

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

[15]  Ronald M. Summers,et al.  Multi-organ Segmentation from Multi-phase Abdominal CT via 4D Graphs Using Enhancement, Shape and Location Optimization , 2010, MICCAI.

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

[17]  Jean Gao,et al.  A deformable model for automatic CT liver extraction. , 2005, Academic radiology.