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9 March 2010 Prediction of polyp histology on CT colonography using content-based image retrieval
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Predicting the malignancy of colonic polyps is a difficult problem and in general requires an invasive polypectomy procedure. We present a less-invasive and automated method to predict the histology of colonic polyps under computed tomographic colonography (CTC) using the content-based image retrieval (CBIR) paradigm. For the purpose of simplification, polyps annotated as hyperplastic or "other benign" were classified as benign polyps (BP) and the rest (adenomas and cancers) were classified as malignant polyps (MP). The CBIR uses numerical feature vectors generated from our CTC computer aided detection (CTC-CAD) system to describe the polyps. These features relate to physical and visual characteristics of the polyp. A representative database of CTC-CAD polyp images is created. Query polyps are matched with those in the database and the results are ranked based on the similarity to the query. Polyps with a majority of representative MPs in their result set are predicted to be malignant and similarly those with a majority of BPs in their results are benign. For evaluation, the system is compared to the typical optical colonoscopy (OC) size based classification. Using receiver operating curve (ROC) analysis, we show our system is sufficiently better than the OC size method.
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Javed M. Aman, Jianhua Yao, and Ronald M. Summers "Prediction of polyp histology on CT colonography using content-based image retrieval", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76240D (9 March 2010);

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