A new 3-D pattern recognition technique with application to computer aided colonoscopy

To utilize CT or MRI images for computer aided diagnosis applications, robust features that represent 3D image data need to be constructed and subsequently used by a classification method. We present a computer aided diagnosis system for early diagnosis of colon cancer. The system extracts features via a new 3D pattern processing method and processes them using a support vector machine classifier. Our 3D pattern processing method, called Random Orthogonal Shape Section (ROSS) mimics the radiologist´s way of viewing these images and combines information from many random triples of mutually orthogonal sections going through the volume. Another contribution of the paper is a new feedback framework between the classification algorithm and the definition of the features. This framework, called Distinctive Component Analysis combines support vector samples with linear discriminant analysis to map the features of clustered support vectors to a lower dimensional space where the two classes of objects of interest are optimally separated to obtain better features. We show that the combination of these better features with support vector machine classification provides a good recognition rate.