A Fast and Accurate Method for Automatic Segmentation of Colons at CT Colonography Based on Colon Geometrical Features

Abstract

In CT colonography, the first major step of colonic polyp detection is reliable segmentation of colon from CT data. In this paper, we propose a fast and accurate method for automatic colon segmentation from CT data using colon geometrical features. After removal of the lung and surrounding air voxels from CT data, labeling is performed to generate candidate regions for Colon segmentation. The centroid of the data, derived from the labeled objects is used to analyze the colon geometry. Other notable features that are used for colon segmentation are volume/length measure and end points. The proposed method was validated using a total of 99 patient datasets. Collapsed colon surface detection was 99.59% with an average of 1.59% extra colonic surface inclusion. The proposed technique takes 16.29 second to segment the colon from an abdomen CT dataset.