Automated feature extraction and identification of colon carcinoma.

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.