Robust segmentation of lung tissue in chest CT scanning
This paper deals with segmentation of the lung tissues from low dose CT (LDCT) scans of the chest. Goal is correct segmentation as well as maintaining the details of the lung region in the chest cavity. In particular, it is essential that the lung nodules inside the lungs as well as on the boundary regions be maintained for subsequent steps that aim at automatic detection and classification of nodules from LDCT scanning; a step for early diagnosis of lung cancer. An approach for segmentation based on combination of EM algorithm and morphological operations is presented. This algorithm is compared with respect two other approaches that are based on level sets and energy optimization by the Graph Cuts technique. Performance evaluation is conducted on a labeled data set from the Early Lung Cancer Action Program (ELCAP) database. The new segmentation approach provides comparable results to Level sets and Graph-Cuts, with the advantage of faster execution time, and minimal user interception.