Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2013, Article ID 419018, 9 pages http://dx.doi.org/10.1155/2013/419018 Research Article Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution I. Cruz-Aceves, 1 J. G. Avina-Cervantes, 1 J. M. Lopez-Hernandez, 1 M. G. Garcia-Hernandez, 1 M. Torres-Cisneros, 1 H. J. Estrada-Garcia, 1 and A. Hernandez-Aguirre 2 1 Divisi´ on de Ingenier´ ıas Campus Irapuato-Salamanca, Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago Km 3.5+1.8 Km Comunidad de Palo Blanco, 36885 Salamanca, GTO, Mexico 2 Centro de Investigaci´ on en Matem´ aticas (CIMAT), A.C. Jalisco S/N, Col. Valenciana, 36000 Guanajuato, GTO, Mexico Correspondence should be addressed to I. Cruz-Aceves; i.cruzaceves@ugto.mx Received 19 July 2013; Accepted 23 October 2013 Academic Editor: Marco Perez-Cisneros Copyright © 2013 I. Cruz-Aceves et al. his 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. his paper presents a novel automatic image segmentation method based on the theory of active contour models and estimation of distribution algorithms. he proposed method uses the univariate marginal distribution model to infer statistical dependencies between the control points on diferent active contours. hese contours have been generated through an alignment process of reference shape priors, in order to increase the exploration and exploitation capabilities regarding diferent interactive segmentation techniques. his proposed method is applied in the segmentation of the hollow core in microscopic images of photonic crystal ibers and it is also used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, respectively. Moreover, to evaluate the performance of the medical image segmentations compared to regions outlined by experts, a set of similarity measures has been adopted. he experimental results suggest that the proposed image segmentation method outperforms the traditional active contour model and the interactive Tseng method in terms of segmentation accuracy and stability. 1. Introduction Automatic image segmentation is an important and chal- lenging problem in computer vision and medical image analysis. he objective of image segmentation is to separate objects of interest from a given image based on diferent attributes such as shape, color, intensity, or texture. In recent years, several techniques have been reported for this purpose including graph cut [1, 2], improved watershed transform [3], suppressed fuzzy c-means [4], supervised fuzzy clustering [5], multithreshold based on diferential evolution [6], and active contour model (ACM), which has been applied in diferent areas such as intravascular ultrasound images [7], automatic urban buildings [8], and natural images [9], to name a few. he Active Contour Model is an energy-minimizing spline curve composed of discrete control points called snaxels. he curve is attracted towards features as edges of a target object through the evaluation of internal and external forces. he classical implementation of ACM is prone to be trapped into local minima problem and it is also highly sensitive to initialization of the control points because they require being close to the target object; otherwise failure of convergence will occur. Since ACM was introduced by [10], many researchers have suggested adapting diferent techniques to work together with ACM in order to overcome its shortcomings. he suggested improvements of the classical ACM including the introduction of prior knowledge such as active shape models [11], shape prior applied on human cerebellum [12], ACM based on level set method [13], population- based methods such as genetic algorithms [14], diferential evolution [15], and particle swarm optimization [16]. he performance of these population-based methods working together with ACM is robust in local minima problem and according to the tests, these methods present a more stable and eicient image segmentation within an appropriate computational time.