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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 479516, 13 pages
http://dx.doi.org/10.1155/2013/479516
Research Article

A Hybrid Method for Pancreas Extraction from CT Image Based on Level Set Methods

1Software College, Northeastern University, Shenyang 110819, China
2Graduate School of Medicine, Gifu University, Yanagido, Gifu 501-1193, Japan

Received 23 May 2013; Revised 4 July 2013; Accepted 18 July 2013

Academic Editor: Kayvan Najarian

Copyright © 2013 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 proposes a novel semiautomatic method to extract the pancreas from abdominal CT images. Traditional level set and region growing methods that request locating initial contour near the final boundary of object have problem of leakage to nearby tissues of pancreas region. The proposed method consists of a customized fast-marching level set method which generates an optimal initial pancreas region to solve the problem that the level set method is sensitive to the initial contour location and a modified distance regularized level set method which extracts accurate pancreas. The novelty in our method is the proper selection and combination of level set methods, furthermore an energy-decrement algorithm and an energy-tune algorithm are proposed to reduce the negative impact of bonding force caused by connected tissue whose intensity is similar with pancreas. As a result, our method overcomes the shortages of oversegmentation at weak boundary and can accurately extract pancreas from CT images. The proposed method is compared to other five state-of-the-art medical image segmentation methods based on a CT image dataset which contains abdominal images from 10 patients. The evaluated results demonstrate that our method outperforms other methods by achieving higher accuracy and making less false segmentation in pancreas extraction.