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Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery

PURPOSE: This paper proposes to characterize the continuum of colorectal cancer (CRC) using multiple texture features extracted from multispectral optical microscopy images. Three types of pathological tissues (PT) are considered: benign hyperplasia, intraepithelial neoplasia and carcinoma. MATERIAL...

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Autores principales: Chaddad, Ahmad, Desrosiers, Christian, Bouridane, Ahmed, Toews, Matthew, Hassan, Lama, Tanougast, Camel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4764026/
https://www.ncbi.nlm.nih.gov/pubmed/26901134
http://dx.doi.org/10.1371/journal.pone.0149893
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author Chaddad, Ahmad
Desrosiers, Christian
Bouridane, Ahmed
Toews, Matthew
Hassan, Lama
Tanougast, Camel
author_facet Chaddad, Ahmad
Desrosiers, Christian
Bouridane, Ahmed
Toews, Matthew
Hassan, Lama
Tanougast, Camel
author_sort Chaddad, Ahmad
collection PubMed
description PURPOSE: This paper proposes to characterize the continuum of colorectal cancer (CRC) using multiple texture features extracted from multispectral optical microscopy images. Three types of pathological tissues (PT) are considered: benign hyperplasia, intraepithelial neoplasia and carcinoma. MATERIALS AND METHODS: In the proposed approach, the region of interest containing PT is first extracted from multispectral images using active contour segmentation. This region is then encoded using texture features based on the Laplacian-of-Gaussian (LoG) filter, discrete wavelets (DW) and gray level co-occurrence matrices (GLCM). To assess the significance of textural differences between PT types, a statistical analysis based on the Kruskal-Wallis test is performed. The usefulness of texture features is then evaluated quantitatively in terms of their ability to predict PT types using various classifier models. RESULTS: Preliminary results show significant texture differences between PT types, for all texture features (p-value < 0.01). Individually, GLCM texture features outperform LoG and DW features in terms of PT type prediction. However, a higher performance can be achieved by combining all texture features, resulting in a mean classification accuracy of 98.92%, sensitivity of 98.12%, and specificity of 99.67%. CONCLUSIONS: These results demonstrate the efficiency and effectiveness of combining multiple texture features for characterizing the continuum of CRC and discriminating between pathological tissues in multispectral images.
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spelling pubmed-47640262016-03-07 Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery Chaddad, Ahmad Desrosiers, Christian Bouridane, Ahmed Toews, Matthew Hassan, Lama Tanougast, Camel PLoS One Research Article PURPOSE: This paper proposes to characterize the continuum of colorectal cancer (CRC) using multiple texture features extracted from multispectral optical microscopy images. Three types of pathological tissues (PT) are considered: benign hyperplasia, intraepithelial neoplasia and carcinoma. MATERIALS AND METHODS: In the proposed approach, the region of interest containing PT is first extracted from multispectral images using active contour segmentation. This region is then encoded using texture features based on the Laplacian-of-Gaussian (LoG) filter, discrete wavelets (DW) and gray level co-occurrence matrices (GLCM). To assess the significance of textural differences between PT types, a statistical analysis based on the Kruskal-Wallis test is performed. The usefulness of texture features is then evaluated quantitatively in terms of their ability to predict PT types using various classifier models. RESULTS: Preliminary results show significant texture differences between PT types, for all texture features (p-value < 0.01). Individually, GLCM texture features outperform LoG and DW features in terms of PT type prediction. However, a higher performance can be achieved by combining all texture features, resulting in a mean classification accuracy of 98.92%, sensitivity of 98.12%, and specificity of 99.67%. CONCLUSIONS: These results demonstrate the efficiency and effectiveness of combining multiple texture features for characterizing the continuum of CRC and discriminating between pathological tissues in multispectral images. Public Library of Science 2016-02-22 /pmc/articles/PMC4764026/ /pubmed/26901134 http://dx.doi.org/10.1371/journal.pone.0149893 Text en © 2016 Chaddad 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 (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Chaddad, Ahmad
Desrosiers, Christian
Bouridane, Ahmed
Toews, Matthew
Hassan, Lama
Tanougast, Camel
Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery
title Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery
title_full Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery
title_fullStr Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery
title_full_unstemmed Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery
title_short Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery
title_sort multi texture analysis of colorectal cancer continuum using multispectral imagery
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4764026/
https://www.ncbi.nlm.nih.gov/pubmed/26901134
http://dx.doi.org/10.1371/journal.pone.0149893
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