Stool Detection and Classification in Colorectal Cancer
This project has been dedicated to the field of medical image analysis concerning the issue of colorectal cancer. Cancers can be evolved in almost any part of the body and is therefore a disease that impacts the whole world. Colorectal cancer is just one of such cancer types and has been coined as one of the more frequent cancer types encountered. Colonoscopy is the accepted screening method for identifying elements known as polyps. Polyps are perceived as swollen tissue found in the colon. Before the search for the polyps begins, an assessment of how clean the bowel is first made to ensure it is safe to identify the above-mentioned elements. This thesis has been focused on detection and classification in order to calculate the percentage of each stool type present in the colon. To address this, k-means clustering was implemented using features such as texture and color to classify the different stool types. Firstly the images were preprocessed, the preprocessing was followed by color segmentation and finally the images were classified. Once the classification of each pixel had been done the classified pixels were assigned a class label. Each label was connected to color and finally a visual representation of the classified image was presented through repainting the entire image. The results show that in a perfect segmentation of the colon the classifier performs well. While in the case of a partial segmentation the frequency of misclassifications increases.
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