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Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 2420962, 9 pages
Research Article

Segmentation of Coronary Angiograms Using Gabor Filters and Boltzmann Univariate Marginal Distribution Algorithm

1Centro de Investigación en Matemáticas (CIMAT), A.C., Jalisco S/N, Col. Valenciana, 36000 Guanajuato, GTO, Mexico
2CONACYT, Centro de Investigación en Matemáticas (CIMAT), A.C., Jalisco S/N, Col. Valenciana, 36000 Guanajuato, GTO, Mexico
3DICIS, Universidad de Guanajuato, Comunidad de Palo Blanco s/n, 36885 Salamanca, GTO, Mexico
4Unidad de Investigación, UMAE 1 Bajío, IMSS, León, GTO, Mexico
5Tecnológico Nacional de México-Instituto Tecnólogico de León, Av. Tecnológico s/n, Fracc. Ind. Julián de Obregón, 37290 León, GTO, Mexico

Received 29 January 2016; Accepted 15 August 2016

Academic Editor: Leonardo Franco

Copyright © 2016 Fernando Cervantes-Sanchez 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 novel method for improving the training step of the single-scale Gabor filters by using the Boltzmann univariate marginal distribution algorithm (BUMDA) in X-ray angiograms. Since the single-scale Gabor filters (SSG) are governed by three parameters, the optimal selection of the SSG parameters is highly desirable in order to maximize the detection performance of coronary arteries while reducing the computational time. To obtain the best set of parameters for the SSG, the area () under the receiver operating characteristic curve is used as fitness function. Moreover, to classify vessel and nonvessel pixels from the Gabor filter response, the interclass variance thresholding method has been adopted. The experimental results using the proposed method obtained the highest detection rate with over a training set of 40 images and with a test set of 40 images. In addition, the experimental results of vessel segmentation provided an accuracy of with the test set of angiograms.