Texture Analysis of Abnormal Cell Images for Predicting the Continuum of Colorectal Cancer.
Abnormal cell (ABC) is a markedly heterogeneous tissue area and can be categorized into three main types: benign hyperplasia (BH), carcinoma (Ca), and intraepithelial neoplasia (IN) or precursor cancerous lesion. In this study, the goal is to determine and characterize the continuum of colorectal cancer by using a 3D-texture approach. ABC was segmented in preprocessing step using an active contour segmentation technique. Cell types were analyzed based on textural features extracted from the gray level cooccurrence matrices (GLCMs). Significant texture features were selected using an analysis of variance (ANOVA) of ABC with apvalue cutoff ofp< 0.01. Features selected were reduced with a principal component analysis (PCA), which accounted for 97% of the cumulative variance from significant features. The simulation results identified 158 significant features based on ANOVA from a total of 624 texture features extracted from GLCMs. Performance metrics of ABC discrimination based on significant texture features showed 92.59% classification accuracy, 100% sensitivity, and 94.44% specificity. These findings suggest that texture features extracted from GLCMs are sensitive enough to discriminate between the ABC types and offer the opportunity to predict cell characteristics of colorectal cancer.
Laboratory of Conception, Optimization and Modelling of Systems, University of Lorraine, 7 rue Marconi, Metz, 57070 Lorraine, France.
Ahmad
0000-0002-3292-7068
A
Chaddad
Laboratory of Conception, Optimization and Modelling of Systems, University of Lorraine, 7 rue Marconi, Metz, 57070 Lorraine, France.
Camel
C
Tanougast
D000465:Algorithms; D003106:Colon / Q000000981:diagnostic imaging* / Q000473:pathology; D015179:Colorectal Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D011230:Precancerous Conditions / Q000000981:diagnostic imaging* / Q000473:pathology; D025341:Principal Component Analysis; D012372:ROC Curve; D012007:Rectum / Q000000981:diagnostic imaging* / Q000473:pathology; D015203:Reproducibility of Results