Lung segmentation is an important first step towards an automated CAD (Computer Aided Detection) system for a variety of medical applications. These applications range from lung nodule detection for identifying cancerous tumors to acinar shadow detection for identifying Tuberculosis. In our prior work we had used the Concave Hull algorithm for lung segmentation. However, our results showed over segmentation. In this work we introduce “Adaptive” concave hulls, combine it with Adaptive Median Filtering, and finally apply an Active Contour Model to make the results much more robust and eliminate the over segmentation and under segmentation problem. Our technique is especially useful for automated detection of Juxtapleural pulmonary nodules that are attached to the chest wall. Experimental results demonstrate the improvements achieved by our new algorithm.