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International Journal of Biomedical Imaging
Volume 2012, Article ID 765649, 13 pages
http://dx.doi.org/10.1155/2012/765649
Research Article

A New GLLD Operator for Mass Detection in Digital Mammograms

1Computer Imaging and Electronic System Group, CEM Laboratory, Department of Electrical Engineering, Sfax Engineering School, University of Sfax, P.O. Box 1169, 3038 Sfax, Tunisia
2El Farabi Radiology Center, 14 Janvier Avenue, 3000 Sfax, Tunisia

Received 19 July 2012; Revised 12 November 2012; Accepted 21 November 2012

Academic Editor: Juan Ruiz-Alzola

Copyright © 2012 N. Gargouri 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

During the last decade, several works have dealt with computer automatic diagnosis (CAD) of masses in digital mammograms. Generally, the main difficulty remains the detection of masses. This work proposes an efficient methodology for mass detection based on a new local feature extraction. Local binary pattern (LBP) operator and its variants proposed by Ojala are a powerful tool for textures classification. However, it has been proved that such operators are not able to model at their own texture masses. We propose in this paper a new local pattern model named gray level and local difference (GLLD) where we take into consideration absolute gray level values as well as local difference as local binary features. Artificial neural networks (ANNs), support vector machine (SVM), and k-nearest neighbors (kNNs) are, then, used for classifying masses from nonmasses, illustrating better performance of ANN classifier. We have used 1000 regions of interest (ROIs) obtained from the Digital Database for Screening Mammography (DDSM). The area under the curve of the corresponding approach has been found to be for the mass detection step. A comparative study with previous approaches proves that our approach offers the best performances.