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Proceedings Paper

Performance comparison of classifiers for differentiation among obstructive lung diseases based on features of texture analysis at HRCT
Author(s): Youngjoo Lee; Joon Beom Seo; Bokyoung Kang; Dongil Kim; June Goo Lee; Song Soo Kim; Namkug Kim; Suk Ho Kang
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Paper Abstract

The performance of classification algorithms for differentiating among obstructive lung diseases based on features from texture analysis using HRCT (High Resolution Computerized Tomography) images was compared. HRCT can provide accurate information for the detection of various obstructive lung diseases, including centrilobular emphysema, panlobular emphysema and bronchiolitis obliterans. Features on HRCT images can be subtle, however, particularly in the early stages of disease, and image-based diagnosis is subject to inter-observer variation. To automate the diagnosis and improve the accuracy, we compared three types of automated classification systems, naïve Bayesian classifier, ANN (Artificial Neural Net) and SVM (Support Vector Machine), based on their ability to differentiate among normal lung and three types of obstructive lung diseases. To assess the performance and cross-validation of these three classifiers, 5 folding methods with 5 randomly chosen groups were used. For a more robust result, each validation was repeated 100 times. SVM showed the best performance, with 86.5% overall sensitivity, significantly different from the other classifiers (one way ANOVA, p<0.01). We address the characteristics of each classifier affecting performance and the issue of which classifier is the most suitable for clinical applications, and propose an appropriate method to choose the best classifier and determine its optimal parameters for optimal disease discrimination. These results can be applied to classifiers for differentiation of other diseases.

Paper Details

Date Published: 26 March 2007
PDF: 12 pages
Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 651249 (26 March 2007); doi: 10.1117/12.710436
Show Author Affiliations
Youngjoo Lee, Seoul National Univ. (South Korea)
Joon Beom Seo, Univ. of Ulsan College of Medicine, Asan Medical Ctr. (South Korea)
Bokyoung Kang, Seoul National Univ. (South Korea)
Dongil Kim, Seoul National Univ. (South Korea)
June Goo Lee, Seoul National Univ. (South Korea)
Song Soo Kim, Univ. of Ulsan College of Medicine, Asan Medical Ctr. (South Korea)
Namkug Kim, Seoul National Univ. (South Korea)
Univ. of Ulsan College of Medicine, Asan Medical Ctr. (South Korea)
Suk Ho Kang, Seoul National Univ. (South Korea)


Published in SPIE Proceedings Vol. 6512:
Medical Imaging 2007: Image Processing
Josien P. W. Pluim; Joseph M. Reinhardt, Editor(s)

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