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Lookup NU author(s): Emeritus Professor Alan MurrayORCiD
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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.
Author(s): Esgiar AN, Naguib RNG, Bennett MK, Murray A
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: ANALYTICAL AND QUANTITATIVE CYTOLOGY AND HISTOLOGY
Year of Conference: 1998
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