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Different Approaches for Extracting Information from the Co-Occurrence Matrix
In 1979 Haralick famously introduced a method for analyzing the texture of an image: a set of statistics extracted from the co-occurrence matrix. In this paper we investigate novel sets of texture descriptors extracted from the co-occurrence matrix; in addition, we compare and combine different stra...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3873395/ https://www.ncbi.nlm.nih.gov/pubmed/24386228 http://dx.doi.org/10.1371/journal.pone.0083554 |
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author | Nanni, Loris Brahnam, Sheryl Ghidoni, Stefano Menegatti, Emanuele Barrier, Tonya |
author_facet | Nanni, Loris Brahnam, Sheryl Ghidoni, Stefano Menegatti, Emanuele Barrier, Tonya |
author_sort | Nanni, Loris |
collection | PubMed |
description | In 1979 Haralick famously introduced a method for analyzing the texture of an image: a set of statistics extracted from the co-occurrence matrix. In this paper we investigate novel sets of texture descriptors extracted from the co-occurrence matrix; in addition, we compare and combine different strategies for extending these descriptors. The following approaches are compared: the standard approach proposed by Haralick, two methods that consider the co-occurrence matrix as a three-dimensional shape, a gray-level run-length set of features and the direct use of the co-occurrence matrix projected onto a lower dimensional subspace by principal component analysis. Texture descriptors are extracted from the co-occurrence matrix evaluated at multiple scales. Moreover, the descriptors are extracted not only from the entire co-occurrence matrix but also from subwindows. The resulting texture descriptors are used to train a support vector machine and ensembles. Results show that our novel extraction methods improve the performance of standard methods. We validate our approach across six medical datasets representing different image classification problems using the Wilcoxon signed rank test. The source code used for the approaches tested in this paper will be available at: http://www.dei.unipd.it/wdyn/?IDsezione=3314&IDgruppo_pass=124&preview=. |
format | Online Article Text |
id | pubmed-3873395 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-38733952014-01-02 Different Approaches for Extracting Information from the Co-Occurrence Matrix Nanni, Loris Brahnam, Sheryl Ghidoni, Stefano Menegatti, Emanuele Barrier, Tonya PLoS One Research Article In 1979 Haralick famously introduced a method for analyzing the texture of an image: a set of statistics extracted from the co-occurrence matrix. In this paper we investigate novel sets of texture descriptors extracted from the co-occurrence matrix; in addition, we compare and combine different strategies for extending these descriptors. The following approaches are compared: the standard approach proposed by Haralick, two methods that consider the co-occurrence matrix as a three-dimensional shape, a gray-level run-length set of features and the direct use of the co-occurrence matrix projected onto a lower dimensional subspace by principal component analysis. Texture descriptors are extracted from the co-occurrence matrix evaluated at multiple scales. Moreover, the descriptors are extracted not only from the entire co-occurrence matrix but also from subwindows. The resulting texture descriptors are used to train a support vector machine and ensembles. Results show that our novel extraction methods improve the performance of standard methods. We validate our approach across six medical datasets representing different image classification problems using the Wilcoxon signed rank test. The source code used for the approaches tested in this paper will be available at: http://www.dei.unipd.it/wdyn/?IDsezione=3314&IDgruppo_pass=124&preview=. Public Library of Science 2013-12-26 /pmc/articles/PMC3873395/ /pubmed/24386228 http://dx.doi.org/10.1371/journal.pone.0083554 Text en © 2013 Nanni et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Nanni, Loris Brahnam, Sheryl Ghidoni, Stefano Menegatti, Emanuele Barrier, Tonya Different Approaches for Extracting Information from the Co-Occurrence Matrix |
title | Different Approaches for Extracting Information from the Co-Occurrence Matrix |
title_full | Different Approaches for Extracting Information from the Co-Occurrence Matrix |
title_fullStr | Different Approaches for Extracting Information from the Co-Occurrence Matrix |
title_full_unstemmed | Different Approaches for Extracting Information from the Co-Occurrence Matrix |
title_short | Different Approaches for Extracting Information from the Co-Occurrence Matrix |
title_sort | different approaches for extracting information from the co-occurrence matrix |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3873395/ https://www.ncbi.nlm.nih.gov/pubmed/24386228 http://dx.doi.org/10.1371/journal.pone.0083554 |
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