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Journal of Healthcare Engineering
Volume 2016, Article ID 8208923, 9 pages
Research Article

A Computer-Aided Detection System for Digital Chest Radiographs

1Computer Science and Systems Department, Faculty of Computer Science, University of Murcia, 30100 Murcia, Spain
2Academic Unit of Engineering, Autonomous University of Guerrero, 39087 Chilpancingo, GRO, Mexico

Received 27 February 2016; Accepted 5 May 2016

Academic Editor: Yinkwee Ng

Copyright © 2016 Juan Manuel Carrillo-de-Gea 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.


Computer-aided detection systems aim at the automatic detection of diseases using different medical imaging modalities. In this paper, a novel approach to detecting normality/pathology in digital chest radiographs is proposed. The problem tackled is complicated since it is not focused on particular diseases but anything that differs from what is considered as normality. First, the areas of interest of the chest are found using template matching on the images. Then, a texture descriptor called local binary patterns (LBP) is computed for those areas. After that, LBP histograms are applied in a classifier algorithm, which produces the final normality/pathology decision. Our experimental results show the feasibility of the proposal, with success rates above 87% in the best cases. Moreover, our technique is able to locate the possible areas of pathology in nonnormal radiographs. Strengths and limitations of the proposed approach are described in the Conclusions.