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Proceedings Paper

Classification of CT examinations for COPD visual severity analysis
Author(s): Jun Tan; Bin Zheng; Xingwei Wang; Jiantao Pu; David Gur; Frank C. Sciurba; J. Ken Leader
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Paper Abstract

In this study we present a computational method of CT examination classification into visual assessed emphysema severity. The visual severity categories ranged from 0 to 5 and were rated by an experienced radiologist. The six categories were none, trace, mild, moderate, severe and very severe. Lung segmentation was performed for every input image and all image features are extracted from the segmented lung only. We adopted a two-level feature representation method for the classification. Five gray level distribution statistics, six gray level co-occurrence matrix (GLCM), and eleven gray level run-length (GLRL) features were computed for each CT image depicted segment lung. Then we used wavelets decomposition to obtain the low- and high-frequency components of the input image, and again extract from the lung region six GLCM features and eleven GLRL features. Therefore our feature vector length is 56. The CT examinations were classified using the support vector machine (SVM) and k-nearest neighbors (KNN) and the traditional threshold (density mask) approach. The SVM classifier had the highest classification performance of all the methods with an overall sensitivity of 54.4% and a 69.6% sensitivity to discriminate "no" and "trace visually assessed emphysema. We believe this work may lead to an automated, objective method to categorically classify emphysema severity on CT exam.

Paper Details

Date Published: 16 April 2012
PDF: 6 pages
Proc. SPIE 8317, Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging, 831723 (16 April 2012); doi: 10.1117/12.911751
Show Author Affiliations
Jun Tan, Washington Univ. in St. Louis (United States)
Bin Zheng, Univ. of Pittsburgh Medical Ctr. (United States)
Xingwei Wang, Univ. of Pittsburgh Medical Ctr. (United States)
Jiantao Pu, Univ. of Pittsburgh Medical Ctr. (United States)
David Gur, Univ. of Pittsburgh Medical Ctr. (United States)
Frank C. Sciurba, Univ. of Pittsburgh Medical Ctr. (United States)
J. Ken Leader, Univ. of Pittsburgh Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 8317:
Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging
Robert C. Molthen; John B. Weaver, Editor(s)

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