Emphysema is a lung disease that occurs as more and more of the walls between air sacs in the lungs get destroyed. Computed tomography (CT) image of the human thorax has been a useful modality for assessing emphysema. Out goal in this paper is to automatically visualize bullaes (continuous low-attenuated region in CT which represents the air-filled region in the CT and therefore the emphysematous lesion in the lung) in the lungs in three dimensions and quantify the emphysema severity of each bullae based on its size and the distance of the bullae from the center of the lungs using fuzzy logic. From the computed emphysema severity score of bullae in the lung, we calculate the overall emphysema severity of the lung by summing up the emphysema severity score of all bullaes in the lung. For the visualization part, we first compute a transparent three-dimensional lung model and from there, we cluster the bullaes using K-means clustering method to see how the bullaes are distributed in groups in the lung. Besides, we compress the three-dimensional lung model along x-, y- and z-axis by assigning the value of every bullae pixel as one and adding up the pixel intensity along x-, y- and z-axis allowing the visualization of the compressed lung from the front, side, and top view, respectively. Consequently, we color the compressed image using continuous multi-valued color code for indicating the severity of the emphysematous destruction in the lung. Our visualization techniques can be used as a medical assistant visualization tool for radiologists.