Authors:
Valentine Wargnier-Dauchelle
;
Camille Simon-Chane
and
Aymeric Histace
Affiliation:
ETIS Lab, ENSEA, 6 Av. du Ponceau, Cergy-Pontoise, France
Keyword(s):
Colorectal Cancer, Video-colonoscopy, Polyp Detection, Computer Aided Diagnosis, Saliency Maps, Bag of Features, SVM, Approximate Entropy.
Abstract:
The detection and removal of adenomatous polyps via colonoscopy is the gold standard for the prevention of colon cancer. Indeed, polyps are at the origins of colorectal cancer which is one of the deadliest diseases in the world. This article aims to contribute to the wide range of methods already developed for the prevention of colorectal cancer risks. For this, the work is organized around the detection and the localization of polyps in video-colonoscopy images. The aim of this paper is to find the best description of a bowel image in order to classify a patch, that is to say a image fragment, as polyp or not. The classification is achieved thanks to an SVM (Support Vector Machine) using a bag of features. Different types of features extraction will be compared. Thus, the traditional SURF (Speeded-Up Robust Features) extractor will be compared to local features extractors like HOG (Histogram of Oriented Gradient) and LBP (Local Binary Pattern) but also to an original extractor based
on the structural entropy.
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