3D automated colon segmentation for efficient polyp detection

With polyps being the main cause of colorectal cancer, accurate colon segmentation is a crucial step for polyp detection in a virtual colonoscopy system. This paper presents a fully automated segmentation framework for the colon which is based on convex formulation of the active contour model. Our approach is tested on 7 sets where the results are further validated for polyp detection. Results show the efficiency of the framework with an overall accuracy of 99%, and high sensitivity of polyp detection.

[1]  Michael E. Zalis,et al.  Digital subtraction bowel cleansing for CT colonography using morphological and linear filtration methods , 2004, IEEE Transactions on Medical Imaging.

[2]  Ronald M. Summers,et al.  Colonic polyp segmentation in CT colonography-based on fuzzy clustering and deformable models , 2004, IEEE Transactions on Medical Imaging.

[3]  Aly A. Farag,et al.  Accurate and fast 3D colon segmentation in CT colonography , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[4]  Aly A. Farag,et al.  Fully automated 3D colon segmentation for early detection of colorectal cancer based on convex formulation of the active contour model , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[5]  J. Koenderink,et al.  Surface perception in pictures , 1992, Perception & psychophysics.

[6]  Hiroyuki Yoshida,et al.  Automated Segmentation of Colonic Walls for Computerized Detection of Polyps in CT Colonography , 2001, Journal of computer assisted tomography.

[7]  Makoto Sato,et al.  An Automatic Colon Segmentation for 3D Virtual Colonoscopy , 2001 .

[8]  Hiroyuki Yoshida,et al.  Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps , 2001, IEEE Transactions on Medical Imaging.

[9]  Aly A. Farag,et al.  Geometric Features Based Framework for Colonic Polyp Detection using a New Color Coding Scheme , 2007, 2007 IEEE International Conference on Image Processing.

[10]  Xavier Bresson,et al.  Fast Global Minimization of the Active Contour/Snake Model , 2007, Journal of Mathematical Imaging and Vision.