title: Computer-aided classification of zoom-endoscopical images using Fourier filters.
abstract: This paper describes an application of machine learning techniques and evolutionary algorithms to colon cancer diagnosis. We propose an automated classification system for endoscopical images, which is supposed to support physicians in making correct decisions. Classification is done according to the pit-pattern scheme, which defines two/six different classes based on the occurrence of patterns on the mucosa. All discriminative information for classification is obtained by filtering an image's frequency domain. A major part of this paper is devoted to the search for proper frequency filters. An extensive experimental study compares different search strategies and the resulting classification accuracies. We result in a top classification accuracy of 96.9% and 86.8% for the two- and six-classes case, respectively, using a database of 484 zoom-endoscopic images. We observe a tendency toward the employment of lower frequency filter structures for the best classification settings.
authors.affiliation: Department of Gastroenterology, Vienna Medical University, Vienna 1090, Austria.
authors.forename: Michael
authors.initials: M
authors.lastname: Häfner
authors.forename: Leonhard
authors.initials: L
authors.lastname: Brunauer
authors.forename: Hannes
authors.initials: H
authors.lastname: Payer
authors.forename: Robert
authors.initials: R
authors.lastname: Resch
authors.forename: Alfred
authors.initials: A
authors.lastname: Gangl
authors.forename: Andreas
authors.initials: A
authors.lastname: Uhl
authors.forename: Friedrich
authors.initials: F
authors.lastname: Wrba
authors.forename: Andreas
authors.initials: A
authors.lastname: Vécsei
mesh_terms: D000465:Algorithms; D003110:Colonic Neoplasms / Q000175:diagnosis; D005583:Fourier Analysis; D006801:Humans