| Summary: | Segmentation is the process through which each pixel in an image is assigned a class. Segmentation may be used to medical image analysis to assist in image-guided surgery, radiation therapy, and better radiological diagnostics. This article summarises our study towards creating a computer-assisted detection (CAD) method for colonoscopy images containing polyps. Our method is a hybrid context-shape strategy that uses shape information to accurately detect polyps and context information to eliminate non-polyp structures. To begin, a basic edge map is generated from a colonoscopy picture. Second, we use our unique feature extraction and edge classification technique to remove non-polyp edges from the edge map. Third, we use our updated edge maps and newly developed voting mechanism to identify polyp candidates with probabilistic confidence ratings. The proposed CAD system was compared to two publicly accessible polyp databases: CVC-ColonDB, which includes 300 colonoscopy pictures with 300 polyp occurrences from 15 different polyps, and ASU-Mayo, which contains 19,400 colonoscopy frames with a total of 5,200 polyp instances from 10 distinct polyps.
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