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Computational and Mathematical Methods in Medicine
Volume 2016 (2016), Article ID 5237191, 8 pages
http://dx.doi.org/10.1155/2016/5237191
Research Article

New Technique for Automatic Segmentation of Blood Vessels in CT Scan Images of Liver Based on Optimized Fuzzy -Means Method

Department of Computer Engineering, Faculty of Engineering, Arak Branch, Islamic Azad University, ARAK, Markazi, Iran

Received 19 July 2016; Revised 23 October 2016; Accepted 27 October 2016

Academic Editor: Thomas Desaive

Copyright © 2016 Katayoon Ahmadi 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

Automatic segmentation of medical CT scan images is one of the most challenging fields in digital image processing. The goal of this paper is to discuss the automatic segmentation of CT scan images to detect and separate vessels in the liver. The segmentation of liver vessels is very important in the liver surgery planning and identifying the structure of vessels and their relationship to tumors. Fuzzy -means (FCM) method has already been proposed for segmentation of liver vessels. Due to classical optimization process, this method suffers lack of sensitivity to the initial values of ​​class centers and segmentation of local minima. In this article, a method based on FCM in conjunction with genetic algorithms (GA) is applied for segmentation of liver’s blood vessels. This method was simulated and validated using 20 CT scan images of the liver. The results showed that the accuracy, sensitivity, specificity, and CPU time of new method in comparison with FCM algorithm reaching up to 91%, 83.62, 94.11%, and 27.17 were achieved, respectively. Moreover, selection of optimal and robust parameters in the initial step led to rapid convergence of the proposed method. The outcome of this research assists medical teams in estimating disease progress and selecting proper treatments.