Endoscopic Image Classification Using Edge-Based Features

We present a system for an automated colon cancer detection based on the pit pattern classification. In contrast to previous work we exploit the visual nature of the underlying classification scheme by extracting features based on detected edges. To focus on the most discriminative subset of features we use a greedy forward feature subset selection. The classification is then carried out using the k-nearest neighbors (k-NN) classifier. The results obtained are very promising and show that an automated classification of the given imagery is feasible by using the proposed method.