ABSTRACT
OBJECTIVE: To assess an automated algorithm, developed for the classification of normal and cancerous colonic mucosa, using geometric analysis of features and texture analysis. STUDY DESIGN: Twenty-one images were analyzed, 10 from normal and 11 from cancerous mucosa. The classification was based on a regularity index dependent on shape, object orientation for establishing parallelism and five texture features derived using the co-occurrence image analysis method. RESULTS: Geometric analysis yielded an overall classification accuracy of 80%. The corresponding sensitivity and specificity were 94% and 64%, respectively. Using texture analysis, the overall classification accuracy was 90%, with a sensitivity and specificity of 82% and 100%, respectively. CONCLUSION: This initial study demonstrated that geometric and texture analysis techniques show promise for automated analysis of colon cancer.
Subject(s)
Carcinoma/diagnosis , Colonic Neoplasms/diagnosis , Colonic Neoplasms/pathology , Image Cytometry/methods , Image Processing, Computer-Assisted/methods , Algorithms , Carcinoma/pathology , Diagnosis, Computer-Assisted/methods , Diagnosis, Computer-Assisted/statistics & numerical data , Humans , Image Cytometry/statistics & numerical data , Image Processing, Computer-Assisted/statistics & numerical data , Predictive Value of Tests , Reproducibility of ResultsABSTRACT
The development of an automated algorithm for the categorization of normal and cancerous colon mucosa is reported. Six features based on texture analysis were studied. They were derived using the co-occurrence matrix and were angular second moment, entropy, contrast, inverse difference moment, dissimilarity, and correlation. Optical density was also studied. Forty-four normal images and 58 cancerous images from sections of the colon were analyzed. These two groups were split equally into two subgroups: one set was used for supervised training and the other to test the classification algorithm. A stepwise selection procedure showed that correlation and entropy were the features that discriminated most strongly between normal and cancerous tissue (P < 0.0001). A parametric linear-discriminate function was used to determine the classification rule. For the training set, a sensitivity and specificity of 93.1% and 81.8%, respectively, were achieved, with an overall accuracy of 88.2%. These results were confirmed with the test set, with a sensitivity and specificity of 93.1% and 86.4%, respectively, and an overall accuracy of 90.2%.
