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Proceedings Paper

Texture feature selection with relevance learning to classify interstitial lung disease patterns
Author(s): Markus B. Huber; Kerstin Bunte; Mahesh B. Nagarajan; Michael Biehl; Lawrence A. Ray; Axel Wismueller
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Paper Abstract

The Generalized Matrix Learning Vector Quantization (GMLVQ) is used to estimate the relevance of texture features in their ability to classify interstitial lung disease patterns in high-resolution computed tomography (HRCT) images. After a stochastic gradient descent, the GMLVQ algorithm provides a discriminative distance measure of relevance factors, which can account for pairwise correlations between different texture features and their importance for the classification of healthy and diseased patterns. Texture features were extracted from gray-level co-occurrence matrices (GLCMs), and were ranked and selected according to their relevance obtained by GMLVQ and, for comparison, to a mutual information (MI) criteria. A k-nearest-neighbor (kNN) classifier and a Support Vector Machine with a radial basis function kernel (SVMrbf) were optimized in a 10-fold crossvalidation for different texture feature sets. In our experiment with real-world data, the feature sets selected by the GMLVQ approach had a significantly better classification performance compared with feature sets selected by a MI ranking.

Paper Details

Date Published: 5 March 2011
PDF: 8 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796318 (5 March 2011); doi: 10.1117/12.877894
Show Author Affiliations
Markus B. Huber, Univ. of Rochester (United States)
Kerstin Bunte, Univ. of Groningen (Netherlands)
Mahesh B. Nagarajan, Univ. of Rochester (United States)
Michael Biehl, Univ. of Groningen (Netherlands)
Lawrence A. Ray, Carestream Health, Inc. (United States)
Axel Wismueller, Univ. of Rochester (United States)


Published in SPIE Proceedings Vol. 7963:
Medical Imaging 2011: Computer-Aided Diagnosis
Ronald M. Summers; Bram van Ginneken, Editor(s)

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