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A New GLLD Operator for Mass Detection in Digital Mammograms

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 bina...

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Detalles Bibliográficos
Autores principales: Gargouri, N., Dammak Masmoudi, A., Sellami Masmoudi, D., Abid, R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3539378/
https://www.ncbi.nlm.nih.gov/pubmed/23365556
http://dx.doi.org/10.1155/2012/765649
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author Gargouri, N.
Dammak Masmoudi, A.
Sellami Masmoudi, D.
Abid, R.
author_facet Gargouri, N.
Dammak Masmoudi, A.
Sellami Masmoudi, D.
Abid, R.
author_sort Gargouri, N.
collection PubMed
description 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 A (z) = 0.95 for the mass detection step. A comparative study with previous approaches proves that our approach offers the best performances.
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spelling pubmed-35393782013-01-30 A New GLLD Operator for Mass Detection in Digital Mammograms Gargouri, N. Dammak Masmoudi, A. Sellami Masmoudi, D. Abid, R. Int J Biomed Imaging Research Article 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 A (z) = 0.95 for the mass detection step. A comparative study with previous approaches proves that our approach offers the best performances. Hindawi Publishing Corporation 2012 2012-12-22 /pmc/articles/PMC3539378/ /pubmed/23365556 http://dx.doi.org/10.1155/2012/765649 Text en Copyright © 2012 N. Gargouri et al. https://creativecommons.org/licenses/by/3.0/ 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.
spellingShingle Research Article
Gargouri, N.
Dammak Masmoudi, A.
Sellami Masmoudi, D.
Abid, R.
A New GLLD Operator for Mass Detection in Digital Mammograms
title A New GLLD Operator for Mass Detection in Digital Mammograms
title_full A New GLLD Operator for Mass Detection in Digital Mammograms
title_fullStr A New GLLD Operator for Mass Detection in Digital Mammograms
title_full_unstemmed A New GLLD Operator for Mass Detection in Digital Mammograms
title_short A New GLLD Operator for Mass Detection in Digital Mammograms
title_sort new glld operator for mass detection in digital mammograms
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3539378/
https://www.ncbi.nlm.nih.gov/pubmed/23365556
http://dx.doi.org/10.1155/2012/765649
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