Segmentation of lung tissue from CT images is a challenging task in computer aided diagnosis systems. In this work, a patient-specific, automated model based approach to segment lung is proposed. Patient specific shape knowledge is obtained by preprocessing the same patient's stack of lung CT slices. The algorithm divides the patient's stack into many groups and the first 'n' slices with maximum lung area from each group are selected and stored in the image database for further processing. Landmark points representing the boundary of the lungs are identified from each slice in the database and stored as shape vectors in the image database. Principal component analysis (PCA) reduces the number of landmark points, retains the major variations in the points in every slice in the database and constructs a shape model called point distribution model (PDM). As the model is generated from the same subject's CT slices, it is initialized at the centroid of the diseased CT slice without manual intervention. The generated shape model can be retained for all the future examinations of the patient. The approach is tested using two datasets: one set with eight tuberculosis CT stacks and the other containing six pneumonia and six lung consolidation CT stacks. The accuracy, average similarity index obtained using the overlap score and dice coefficient for both sets are (97%, 97.9%), (0.970, 0.962) and (0.985, 0.983) respectively. The results show that the approach used in model construction to segment lungs from CT image slices has greatly improved segmentation accuracy. © 2016, Institute of Information Science. All rights reserved.