title: Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery.
abstract: OBJECTIVE
  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.
  
  
  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.
authors.affiliation: Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure, Montréal, Québec, Canada.|Laboratory of Conception, Optimization and Modelling of Systems, University of Lorraine, Metz, Lorraine, France.
authors.forename: Ahmad
authors.initials: A
authors.lastname: Chaddad
authors.affiliation: Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure, Montréal, Québec, Canada.
authors.forename: Christian
authors.initials: C
authors.lastname: Desrosiers
authors.affiliation: School of Computing, Engineering and Information Sciences, Northumbria University, Newcastle, United Kingdom.
authors.forename: Ahmed
authors.initials: A
authors.lastname: Bouridane
authors.affiliation: Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure, Montréal, Québec, Canada.
authors.forename: Matthew
authors.initials: M
authors.lastname: Toews
authors.affiliation: Laboratory of Conception, Optimization and Modelling of Systems, University of Lorraine, Metz, Lorraine, France.
authors.forename: Lama
authors.initials: L
authors.lastname: Hassan
authors.affiliation: Laboratory of Conception, Optimization and Modelling of Systems, University of Lorraine, Metz, Lorraine, France.
authors.forename: Camel
authors.initials: C
authors.lastname: Tanougast
mesh_terms: D015179:Colorectal Neoplasms / Q000473:pathology*; D003952:Diagnostic Imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted; D066298:In Vitro Techniques