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Multi texture analysis of colorectal cancer continuum using multispectral imagery


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Chaddad, Ahmad, Desrosiers, Christian, Bouridane, Ahmed, Toews, Matthew, Hassan, Lama et Tanougast, Camel. 2016. « Multi texture analysis of colorectal cancer continuum using multispectral imagery ». PLoS ONE, vol. 11, nº 2.
Compte des citations dans Scopus : 22.

Toews M. 2016 12391 Multi Texture Analysis of Colorectal Cancer Contimuum.PDF - Published Version
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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.

Item Type: Peer reviewed article published in a journal
Uncontrolled Keywords: Fonds d'auteur ÉTS, FAETS
Desrosiers, Christian
Toews, Matthew
Affiliation: Génie logiciel et des technologies de l'information, Génie de la production automatisée
Date Deposited: 02 Mar 2016 16:50
Last Modified: 17 Jan 2020 21:03

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