Texture feature selection with relevance learning to classify interstitial lung disease patterns

Markus B. Huber; Kerstin Bunte; Mahesh B. Nagarajan; Michael Biehl; Lawrence A. Ray; Axel Wismueller

doi:10.1117/12.877894

4 March 2011 Texture feature selection with relevance learning to classify interstitial lung disease patterns

Markus B. Huber, Kerstin Bunte, Mahesh B. Nagarajan, Michael Biehl, Lawrence A. Ray, Axel Wismueller

Proceedings Volume 7963, Medical Imaging 2011: Computer-Aided Diagnosis; 796318 (2011) https://doi.org/10.1117/12.877894
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

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.

Citation Download Citation

Markus B. Huber, Kerstin Bunte, Mahesh B. Nagarajan, Michael Biehl, Lawrence A. Ray, and Axel Wismueller "Texture feature selection with relevance learning to classify interstitial lung disease patterns", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796318 (4 March 2011); https://doi.org/10.1117/12.877894

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available