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Chronic obstructive pulmonary disease (COPD) is a disease in which the airways and tiny air sacs (alveoli) inside the lungs are partially obstructed or destroyed. Emphysema is what occurs as more and more of the walls between air sacs get destroyed. Computed tomography (CT) image has been a useful modality for assessing diffuse lung diseases, particularly, emphysema. At present, diagnosis of emphysema is done by using spirometry, X-rays, spiral chest computed tomography (CT)-scan, bronchoscopy, blood tests and pulse oximetry. In this study, we extracted the two-dimensional emphysematous lung tissues in the lung CT automatically using digital image processing techniques, then we visualized the extracted emphysematous lung tissues by implementing a three-dimensional (3D) lung model which was computed using 55 pre-processed CT images, and finally we divided the lung model into eight sub-volumes and classified each sub-volume into five classes of emphysema related severity using an artificial neural network. The performance of the classifier was assessed using the leave-one-out method on 120 sub-volumes of the lungs generated from 15 COPD-verified patients' CT data sets.