Multi texture analysis of colorectal cancer continuum using multispectral imagery

Chaddad, Ahmad, Desrosiers, Christian, Bouridane, Ahmed, Toews, Matthew, Hassan, Lama and Tanougast, Camel (2016) Multi texture analysis of colorectal cancer continuum using multispectral imagery. PLoS ONE, 11 (2). ISSN 1932-6203

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Official URL: http://dx.doi.org/10.1371/journal.pone.0149893

Abstract

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: Article
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer Science and Digital Technologies
Depositing User: Nicola King
Date Deposited: 24 Mar 2016 12:35
Last Modified: 16 May 2017 23:12
URI: http://nrl.northumbria.ac.uk/id/eprint/26439

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