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. MATERIALS...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2016-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC4764026?pdf=render |
id |
doaj-1e6a3b12cff94cf095b0223af6217fcd |
---|---|
record_format |
Article |
spelling |
doaj-1e6a3b12cff94cf095b0223af6217fcd2020-11-25T01:26:49ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01112e014989310.1371/journal.pone.0149893Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery.Ahmad ChaddadChristian DesrosiersAhmed BouridaneMatthew ToewsLama HassanCamel TanougastPURPOSE: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.http://europepmc.org/articles/PMC4764026?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ahmad Chaddad Christian Desrosiers Ahmed Bouridane Matthew Toews Lama Hassan Camel Tanougast |
spellingShingle |
Ahmad Chaddad Christian Desrosiers Ahmed Bouridane Matthew Toews Lama Hassan Camel Tanougast Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery. PLoS ONE |
author_facet |
Ahmad Chaddad Christian Desrosiers Ahmed Bouridane Matthew Toews Lama Hassan Camel Tanougast |
author_sort |
Ahmad Chaddad |
title |
Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery. |
title_short |
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_sort |
multi texture analysis of colorectal cancer continuum using multispectral imagery. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2016-01-01 |
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. |
url |
http://europepmc.org/articles/PMC4764026?pdf=render |
work_keys_str_mv |
AT ahmadchaddad multitextureanalysisofcolorectalcancercontinuumusingmultispectralimagery AT christiandesrosiers multitextureanalysisofcolorectalcancercontinuumusingmultispectralimagery AT ahmedbouridane multitextureanalysisofcolorectalcancercontinuumusingmultispectralimagery AT matthewtoews multitextureanalysisofcolorectalcancercontinuumusingmultispectralimagery AT lamahassan multitextureanalysisofcolorectalcancercontinuumusingmultispectralimagery AT cameltanougast multitextureanalysisofcolorectalcancercontinuumusingmultispectralimagery |
_version_ |
1725108773601673216 |