A New GLLD Operator for Mass Detection in Digital Mammograms. (22nd December 2012)

• Record Type: Journal Article
• Authors: Gargouri, N.; Dammak Masmoudi, A.; Sellami Masmoudi, D.; Abid, R.
• Other Names: Ruiz-Alzola Juan Academic Editor.
• Is Part Of: International journal of biomedical imaging. Volume 2012(2012)
• Publication Date: 2012-12-22
• Subjects: Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Imagerie pour le diagnostic; Imagerie médicale; Diagnostic imaging; Imaging systems in medicine; Diagnostic Imaging -- Periodicals; Electronic journals; Periodicals; 616.0754
• DOI: 10.1155/2012/765649
• Languages: English
• ISSNs: 1687-4188

Abstract

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 beA z = 0.95 for the mass detection step. A comparative study with previous approaches proves that our approach offers the best performances.