Assisted Diagnostic System for Identifying Gastrointestinal Cancer using Adapted Color Features

Abstract

This work is a result of discernment of the fact that the implementation of computer aided design (CAD) for the diagnosis of cancer in gastrointestinal (GI) tract has prospective towards a vigorous effort for the betterment of mankind. There exists multiple levels of relevant literature and research for the configuration of CAD system. We aimed to unfold the contribution of a computer vision module in the prototype of this system. The basic infrastructure of the this module finds its strongest roots to rely on the layout of pattern recognition; which encompasses classification of images all the way from image restoration, to segmentation followed by feature extraction. We have studied the general characteristics of endoscopic images to comprehend the requisites, constraints and challenges towards the development of this module. Later we made our research more specific by particularly focusing on Chromoendoscopy imaging modality for GI tract. Clinically pertinent images were filtered out from the pool of images, recorded during live endoscopic scrutiny. We have devised a novel method for color feature extraction using adapted color space and exploiting classical bag-of-words approach. Aware of the fact, that the dynamic imaging conditions are inevitable, we encountered the issue of illumination variation in the images; that was tackled using homomorphic filtering. Later, we integrated color features with texture using Homogenous Texture descriptor based on the Gabor filter responses. We, then verified the research with results affirming the claims to be generic, robust and reliable. In order to prove the better performance of the proposed descriptor, a comparison between different descriptors was drawn.