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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 190304, 14 pages
Research Article

Multiple Active Contours Guided by Differential Evolution for Medical Image Segmentation

Universidad de Guanajuato, Division de Ingenierias Campus, Irapuato-Salamanca, Carretera Salamanca-Valle de Santiago, Km 3.5+1.8 Km Comunidad de Palo Blanco, C.P. 36885, Salamanca, GTO, Mexico

Received 14 February 2013; Accepted 19 June 2013

Academic Editor: J. M. Górriz

Copyright © 2013 I. Cruz-Aceves 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.


This paper presents a new image segmentation method based on multiple active contours guided by differential evolution, called MACDE. The segmentation method uses differential evolution over a polar coordinate system to increase the exploration and exploitation capabilities regarding the classical active contour model. To evaluate the performance of the proposed method, a set of synthetic images with complex objects, Gaussian noise, and deep concavities is introduced. Subsequently, MACDE is applied on datasets of sequential computed tomography and magnetic resonance images which contain the human heart and the human left ventricle, respectively. Finally, to obtain a quantitative and qualitative evaluation of the medical image segmentations compared to regions outlined by experts, a set of distance and similarity metrics has been adopted. According to the experimental results, MACDE outperforms the classical active contour model and the interactive Tseng method in terms of efficiency and robustness for obtaining the optimal control points and attains a high accuracy segmentation.