title: Development of an automatic classification system for differentiation of obstructive lung disease using HRCT.
abstract: The motivation is to introduce new shape features and optimize the classifier to improve performance of differentiating obstructive lung diseases, based on high-resolution computerized tomography (HRCT) images. Two hundred sixty-five HRCT images from 82 subjects were selected. On each image, two experienced radiologists selected regions of interest (ROIs) representing area of severe centrilobular emphysema, mild centrilobular emphysema, bronchiolitis obliterans, or normal lung. Besides 13 textural features, additional 11 shape features were employed to evaluate the contribution of shape features. To optimize the system, various ROI size (16 x 16, 32 x 32, and 64 x 64 pixels) and other classifier parameters were tested. For automated classification, the Bayesian classifier and support vector machine (SVM) were implemented. To assess cross-validation of the system, a five-folding method was used. In the comparison of methods employing only the textural features, adding shape features yielded the significant improvement of overall sensitivity (7.3%, 6.1%, and 4.1% in the Bayesian and 9.1%, 7.5%, and 6.4% in the SVM, in the ROI size 16 x 16, 32 x 32, 64 x 64 pixels, respectively; t test, P < 0.01). After feature selection, most of cluster shape features were survived ,and the feature selected set shows better performance of the overall sensitivity (93.5 +/- 1.0% in the SVM in the ROI size 64 x 64 pixels; t test, P < 0.01). Adding shape features to conventional texture features is much useful to improve classification performance of obstructive lung diseases in both Bayesian and SVM classifiers. In addition, the shape features contribute more to overall sensitivity in smaller ROI.
authors: Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 388-1, Pungnap2-dong, Songpa-gu, Seoul, 138-736, Republic of Korea.
authors: Namkug
authors: N
authors: Kim
authors: Joon Beom
authors: JB
authors: Seo
authors: Youngjoo
authors: Y
authors: Lee
authors: June Goo
authors: JG
authors: Lee
authors: Song Soo
authors: SS
authors: Kim
authors: Suk-Ho
authors: SH
authors: Kang
mesh_terms: D001499:Bayes Theorem; D001989:Bronchiolitis Obliterans / Q000145:classification / Q000000981:diagnostic imaging; D003937:Diagnosis, Differential; D006801:Humans; D008173:Lung Diseases, Obstructive / Q000145:classification* / Q000000981:diagnostic imaging*; D015588:Observer Variation; D011656:Pulmonary Emphysema / Q000145:classification / Q000000981:diagnostic imaging; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D014057:Tomography, X-Ray Computed / Q000379:methods*