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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2012
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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. |
format | Online Article Text |
id | pubmed-3539378 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
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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