Publication at Faculty of Mathematics and Physics |
2015
Abstract
In this paper we present an automatic algorithm to segment and track small intestine from CT enterography. The algorithm can handle noisy thin-slice data and is adaptable to the greatly varying spatial structure of the organ.
Our approach automatically segments all well-distended parts and performs tracking of the intestinal path. Pre-filtered data are segmented with watershed segmentation and then a kNN-based probability function enhances whole parts of the lumen.
Post-process based on a robust form of region growing is then used for path tracking.