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Automated feature extraction and identification of colon carcinoma
A N Esgiar1, R N Naguib, M K Bennett
1Department of Electrical and Electronic Engineering, Newcastle University, Newcastle upon Tyne, U.K.
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.