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Type: Conference paper
Title: Feature subset selection using ICA for classifying emphysema in HRCT images
Author: Prasad, M.
Sowmya, A.
Koch, I.
Citation: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, August 23–26, 2004, Cambridge UK / J. Kittler, M. Petrou, M. Nixon (eds.): volume 4: pp. 515-518
Publisher: IEEE
Publisher Place: on-line
Issue Date: 2004
ISBN: 0769521282
Conference Name: International Conference on Pattern Recognition (17th : 2004 : Cambridge, UK)
Statement of
Mithun Nagendra Prasad, Arcot Sowmya and Inge Koch
Abstract: Feature subset selection, applied as a pre-processing step to machine learning, is valuable in dimensionality reduction, eliminating irrelevant data and improving classifier performance. In recent years, data in some applications has increased in both the number of instances and features. It is in this context that we introduce a novel approach to reduce both instance and feature space through independent component analysis (ICA) for the classification of emphysema in high resolution computer tomography (HRCT) images. The technique was tested successfully on 60 HRCT scans having emphysema using three different classifiers (Naive Bayes, C4.5 and Seeded K Means). The results were also compared against "density mask", a standard approach used for emphysema detection in medical image analysis. In addition, the results were visually validated by radiologists.
Description: ©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
DOI: 10.1109/ICPR.2004.1333824
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Mathematical Sciences publications

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