title: Automatic polyp detection: A comparative study.
abstract: In this work we present a performance comparison between a set of different state-of-the-art image descriptors for the automatic detection of polyps in colonoscopy videos. This set includes: Local binary patterns, 2-dimensional Gabor filters, wavelet-based texture, and histogram of oriented gradients. We use these descriptors in conjunction with support vector machine or nearest neighbor classifiers to classify candidate regions, which in turn are selected using the maximally stable extremal regions algorithm. We present performance scores on the ASU-Mayo Clinic polyp database.
authors.forename: Alaa
authors.initials: A
authors.lastname: El Khatib
authors.forename: Naoufel
authors.initials: N
authors.lastname: Werghi
authors.forename: Hussain
authors.initials: H
authors.lastname: Al-Ahmad
mesh_terms: D000465:Algorithms; D003111:Colonic Polyps; D003113:Colonoscopy; D006801:Humans; D010363:Pattern Recognition, Automated; D060388:Support Vector Machine