Automatic categorization of medical images for content-based retrieval and data mining
Inhaltsverzeichnis
Referenz
Thomas M. Lehmann,Mark O.Guld, Thomas Deselaers, Daniel Keysers,Henning Schubert, Klaus Spitzer, Hermann Ney and Berthold B. Wein: Automatic categorization of medical images for content-based retrieval and data mining. Computerized Medical Imaging and Graphics, Vol.29, No.2, pp.143-155, 2005
DOI
http://dx.doi.org/10.1016/j.compmedimag.2004.09.010
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.
Extended Abstract
Bibtex
@article{
author = {Thomas M. Lehmann,Mark O.Guld, Thomas Deselaers, Daniel Keysers,Henning Schubert, Klaus Spitzer, Hermann Ney and Berthold B. Wein},
title = {Automatic categorization of medical images for content-based retrieval and data mining},
journal = {Computerized Medical Imaging and Graphics},
volume = {29},
number = {2},
pages = {143-155},
year = {2005},
doi={},
url={http://de.evo-art.org/index.php?title=Automatic_categorization_of_medical_images_for_content-based_retrieval_and_data_mining},
}
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