Accurate and fast 3D colon segmentation in CT colonography

Authors:
Dongqing Chen;Rachid Fahmi;Aly A. Farag;Robert L. Falk;Gerald W. Dryden
Affiliations:
Computer Vision & Image Processing Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY;Computer Vision & Image Processing Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY;Computer Vision & Image Processing Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY;Department of Medical Imaging, Jewish Hospital & St. Mary's Healthcare, Louisville, KY;Department of Medicine, University of Louisville, Louisville, KY
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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Abstract

This paper introduces an adaptive level set method for 3D segmentation of colon tissue in CT colonography filled with air and opacified fluid. First, most of the opacified liquid is removed by a threshold value. The closed contours are propagated toward the desired 3D region boundaries through the iterative evolution of the adaptive level sets function. The proposed method has been tested on 22 real CT colonography datasets with various pathologies, and the segmentation accuracy has achieved 98.40%.