Classification of interstitial lung disease patterns using local DCT features and random forest.
Publisher DOI
PubMed ID
25571374
Abstract
Over the last decade, a plethora of computer-aided diagnosis (CAD) systems have been proposed aiming to improve the accuracy of the physicians in the diagnosis of interstitial lung diseases (ILD). In this study, we propose a scheme for the classification of HRCT image patches with ILD abnormalities as a basic component towards the quantification of the various ILD patterns in the lung. The feature extraction method relies on local spectral analysis using a DCT-based filter bank. After convolving the image with the filter bank, q-quantiles are computed for describing the distribution of local frequencies that characterize image texture. Then, the gray-level histogram values of the original image are added forming the final feature vector. The classification of the already described patches is done by a random forest (RF) classifier. The experimental results prove the superior performance and efficiency of the proposed approach compared against the state-of-the-art.
Date Issued
2014
Publication Type
Article
Language(s)
en
Author(s)
Journal
IEEE Engineering in Medicine and Biology Society conference proceedings
Publisher
IEEE Service Center
ISSN
1557-170X
Access(Rights)
restricted