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http://dx.doi.org/10.5392/JKCA.2011.11.11.185

Computer-Aided Diagnosis for Pulmonary Tuberculosis using Texture Features Analysis in Digital Chest Radiography  

Kim, Dae-Hun (부산가톨릭대학교 생명과학대학원 방사선학과)
Ko, Seong-Jin (부산가톨릭대학교 보건과학대학 방사선학과)
Kang, Se-Sik (부산가톨릭대학교 보건과학대학 방사선학과)
Kim, Jung-Hoon (부산가톨릭대학교 보건과학대학 방사선학과)
Kim, Chang-Soo (부산가톨릭대학교 보건과학대학 방사선학과)
Publication Information
The Journal of the Korea Contents Association / v.11, no.11, 2011 , pp. 185-193 More about this Journal
Abstract
There is no exact standard of detecting pulmonary tuberculosis(TB) in digital image of simple chest radiography. In this study, I experimented on the principal components analysis(PCA) algorithm in the past and suggested six other parameters as identification of TB lesions. The purpose of this study was to develop and test computer aided diagnosis(detection) method for the detection and measurement of pulmonary abnormalities on digital chest radiography. It showed comparatively low recognition diagnosis rate using PCA method, however, six kinds of texture features parameters algorithm showed similar or higher diagnosis rates of pulmonary disease than that of the clinical radiologists. Proposed algorithms using computer-aided of texture analysis can distinguish between areas of abnormality in the chest digital images, differentiate lesions having pulmonary disease. The method could be useful tool for classifying and measuring chest lesions, it would play a major role in radiologist's diagnosis of disease so as to help in pre-reading diagnosis and prevention of pulmonary tuberculosis.
Keywords
Pulmonary Tuberculosis; Computer Aided Diagnosis; Principal Component Analysis; Texture Feature;
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