Identifying Potentially Cancerous Tissues in Chromoendoscopy Images

摘要

The dynamics of image acquisition conditions for gastroen-terology imaging scenarios pose novel challenges for automatic computer assisted decision systems. Such systems should have the ability to mimic the tissue characterization of the physicians. In this paper, our objective is to compare some feature extraction methods to classify a Chromoendoscopy image into two different classes: Normal and Potentially cancerous. Results show that LoG niters generally give best classification accuracy among the other feature extraction methods considered.