J Korean Radiol Soc.
2007 Jul;57(1):21-26. 10.3348/jkrs.2007.57.1.21.
An Automated Classification System for the Differentiation of Obstructive Lung Diseases based on the Textural Analysis of HRCT images
- Affiliations
-
- 1Department of Radiology, Research Institute of Radiology, University of Ulsan, College of Medicine, Asan Medical Center, Korea. seojb@amc.seoul.kr
- 2Department of Radiology, Wonkwang University College of Medicine, Korea.
- 3Department of Radiology, Seoul National University College of Medicine, Korea.
- 4Department of Radiology, University of Pocheon Jungmoon College of Medicine, Bundang CHA Hospital, Korea.
- 5Department of Radiology, Chungnam National University Hospital, Korea.
- KMID: 1834476
- DOI: http://doi.org/10.3348/jkrs.2007.57.1.21
Abstract
- PURPOSE
To develop an automated classification system for the differentiation of obstructive lung diseases based on the textural analysis of HRCT images, and to evaluate the accuracy and usefulness of the system.
MATERIALS AND METHODS
For textural analysis, histogram features, gradient features, run length encoding, and a co-occurrence matrix were employed. A Bayesian classifier was used for automated classification. The images (image number n=256) were selected from the HRCT images obtained from 17 healthy subjects (n=67), 26 patients with bronchiolitis obliterans (n=70), 28 patients with mild centrilobular emphysema (n=65), and 21 patients with panlobular emphysema or severe centrilobular emphysema (n=63). An five-fold cross-validation method was used to assess the performance of the system. Class-specific sensitivities were analyzed and the overall accuracy of the system was assessed with kappa statistics.
RESULTS
The sensitivity of the system for each class was as follows: normal lung 84.9%, bronchiolitis obliterans 83.8%, mild centrilobular emphysema 77.0%, and panlobular emphysema or severe centrilobular emphysema 95.8%. The overall performance for differentiating each disease and the normal lung was satisfactory with a kappa value of 0.779.
CONCLUSION
An automated classification system for the differentiation between obstructive lung diseases based on the textural analysis of HRCT images was developed. The proposed system discriminates well between the various obstructive lung diseases and the normal lung.
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