title: Automatic categorization of medical images for content-based retrieval and data mining.
abstract: Categorization of medical images means selecting the appropriate class for a given image out of a set of pre-defined categories. This is an important step for data mining and content-based image retrieval (CBIR). So far, published approaches are capable to distinguish up to 10 categories. In this paper, we evaluate automatic categorization into more than 80 categories describing the imaging modality and direction as well as the body part and biological system examined. Based on 6231 reference images from hospital routine, 85.5% correctness is obtained combining global texture features with scaled images. With a frequency of 97.7%, the correct class is within the best ten matches, which is sufficient for medical CBIR applications.
authors.affiliation: Department of Medical Informatics, Medical Faculty, Aachen University of Technology (RWTH), Pauwelsstrasse 30, Aachen D-52057, Germany. lehmann@computer.org
authors.forename: Thomas M
authors.initials: TM
authors.lastname: Lehmann
authors.forename: Mark O
authors.initials: MO
authors.lastname: Güld
authors.forename: Thomas
authors.initials: T
authors.lastname: Deselaers
authors.forename: Daniel
authors.initials: D
authors.lastname: Keysers
authors.forename: Henning
authors.initials: H
authors.lastname: Schubert
authors.forename: Klaus
authors.initials: K
authors.lastname: Spitzer
authors.forename: Hermann
authors.initials: H
authors.lastname: Ney
authors.forename: Berthold B
authors.initials: BB
authors.lastname: Wein
mesh_terms: D001331:Automation; D003952:Diagnostic Imaging; D005858:Germany; D016247:Information Storage and Retrieval