Automatic Colonic Polyp Shape Determination using Content-Based Image Retrieval

Polyp shape (sessile or pedunculated) may provide important clinical implication. However, the traditional way of determining polyp shape is both invasive and subjective. We present a less-invasive and automated method to predict the shape of colonic polyps on computed tomographic colonography (CTC) using the content-based image retrieval (CBIR) approach. We classify polyps as either sessile (SS) or pedunculated (PS) in shape. 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. Feature selection was done using a support vector machine classifier on a training set of polyp shapes. The system is evaluated using an independent test set. Using receiver operating curve (ROC) analysis, we showed our system is as accurate as a polyp shape classifier. The area under the ROC curve was 0.86 (95% confidence interval [0.77, 0.93]).