The purpose of this study is to use modern image segmentation techniques to quantitate cyst area and number within a complete CT examination of the lungs. Lymphangioleiomyomatosis (LAM) was chosen because this disease produces many well defined thin- walled cysts of varying sizes throughout the lungs that provide a good test for 2D image segmentation techniques, which are used to separate LAM cysts from the normal lung tissue. Quantitative measures of the lung, such as cyst area versus frequency, are then automatically extracted. Three women with LAM were examined using CT slices obtained at 20 mm intervals, with 1 to 1.5 mm collimation, and a pixel size of 0.4 - 0.5 mm. Our segmentation algorithm operates in several stages. First, masks for each lung are automatically generated, thus allowing only lung pixels to be considered for the cyst segmentation. Next, we threshold the data under the masks at a level of -900 Hounsfield units. The threshold segments LAM cysts from normal lung tissue and other structures, such as pulmonary veins and arteries. In order to determine the size of individual cysts, we grow all regions having brightness values lower than the threshold within the masked regions. These regions, which correspond to cysts, are then sorted by size, and a cyst histogram for each patient is computed.