Performance testing of several classifiers for differentiating obstructive lung diseases based on texture analysis at high-resolution computerized tomography (HRCT)

Authors:
Youngjoo Lee;Joon Beom Seo;June Goo Lee;Song Soo Kim;Namkug Kim;Suk Ho Kang
Affiliations:
Department of Industrial Engineering, Seoul National University, Seoul 151-742, Republic of Korea;Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul 138-736, Republic of Korea;Department of Radiology, Seoul National University Hospital, Seoul 110-744, Republic of Korea;Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul 138-736, Republic of Korea;Department of Industrial Engineering, Seoul National University, Seoul 151-742, Republic of Korea and Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medic ...;Department of Industrial Engineering, Seoul National University, Seoul 151-742, Republic of Korea
Venue:
Computer Methods and Programs in Biomedicine
Year:
2009

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Abstract

Machine classifiers have been used to automate quantitative analysis and avoid intra-inter-reader variability in previous studies. The selection of an appropriate classification scheme is important for improving performance based on the characteristics of the data set. This paper investigated the performance of several machine classifiers for differentiating obstructive lung diseases using texture analysis on various ROI (region of interest) sizes. 265 high-resolution computerized tomography (HRCT) images were taken from 92 subjects. On each image, two experienced radiologists selected ROIs with various sizes representing area of severe centrilobular emphysema (PLE, n=63), mild centrilobular emphysema (CLE, n=65), bronchiolitis obliterans (BO, n=70) or normal lung (NL, n=67). Four machine classifiers were implemented: naive Bayesian classifier, Bayesian classifier, ANN (artificial neural net) and SVM (support vector machine). For a testing method, 5-fold cross-validation methods were used and each validation was repeated 20 times. The SVM had the best performance in overall accuracy (in ROI size of 32x32 and 64x64) (t-test, p