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14 April 2005 Sensitivity and specificity of 3-D texture analysis of lung parenchyma is better than 2-D for discrimination of lung pathology in stage 0 COPD
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Abstract
Lung parenchyma evaluation via multidetector-row CT (MDCT), has significantly altered clinical practice in the early detection of lung disease. Our goal is to enhance our texture-based tissue classification ability to differentiate early pathologic processes by extending our 2-D Adaptive Multiple Feature Method (AMFM) to 3-D AMFM. We performed MDCT on 34 human volunteers in five categories: emphysema in severe Chronic Obstructive Pulmonary Disease (COPD) as EC, emphysema in mild COPD (MC), normal appearing lung in COPD (NC), non-smokers with normal lung function (NN), smokers with normal function (NS). We volumetrically excluded the airway and vessel regions, calculated 24 volumetric texture features for each Volume of Interest (VOI); and used Bayesian rules for discrimination. Leave-one-out and half-half methods were used for testing. Sensitivity, specificity and accuracy were calculated. The accuracy of the leave-one-out method for the four-class classification in the form of 3-D/2-D is: EC: 84.9%/70.7%, MC: 89.8%/82.7%; NC: 87.5.0%/49.6%; NN: 100.0%/60.0%. The accuracy of the leave-one-out method for the two-class classification in the form of 3-D/2-D is: NN: 99.3%/71.6%; NS: 99.7%/74.5%. We conclude that 3-D AMFM analysis of the lung parenchyma improves discrimination compared to 2-D analysis of the same images.
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Ye Xu, Milan Sonka, Geoffrey McLennan, Junfeng Guo, and Eric Hoffman "Sensitivity and specificity of 3-D texture analysis of lung parenchyma is better than 2-D for discrimination of lung pathology in stage 0 COPD", Proc. SPIE 5746, Medical Imaging 2005: Physiology, Function, and Structure from Medical Images, (14 April 2005); https://doi.org/10.1117/12.595879
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