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BioMed Research International
Volume 2014 (2014), Article ID 769751, 11 pages
http://dx.doi.org/10.1155/2014/769751
Research Article

A Priori Knowledge and Probability Density Based Segmentation Method for Medical CT Image Sequences

1Software College, Northeastern University, Shenyang 110819, China
2Radiology Department, PLA General Hospital, Shenyang 110016, China

Received 4 October 2013; Accepted 28 April 2014; Published 19 May 2014

Academic Editor: Huiru Zheng

Copyright © 2014 Huiyan Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper briefly introduces a novel segmentation strategy for CT images sequences. As first step of our strategy, we extract a priori intensity statistical information from object region which is manually segmented by radiologists. Then we define a search scope for object and calculate probability density for each pixel in the scope using a voting mechanism. Moreover, we generate an optimal initial level set contour based on a priori shape of object of previous slice. Finally the modified distance regularity level set method utilizes boundaries feature and probability density to conform final object. The main contributions of this paper are as follows: a priori knowledge is effectively used to guide the determination of objects and a modified distance regularization level set method can accurately extract actual contour of object in a short time. The proposed method is compared to other seven state-of-the-art medical image segmentation methods on abdominal CT image sequences datasets. The evaluated results demonstrate our method performs better and has the potential for segmentation in CT image sequences.