Automatic Segmentation of Pulmonary Lobe Using Marker Based Watershed Algorithm

Segmentation is an important process in the field of medical imaging, as it can provide detailed information of an image. In this work, segmentation of pulmonary lobe is carried out which is useful for the clinical interpretation of CT images, to access the early presence and the characterization of several lung diseases. This segmentation process is challenging for severely diseased lung or lung with incomplete fissures. Existing methods highly rely on the detection of fissures whereas, this technique becomes less reliable in cases of abnormalities. In order to overcome this, automatic segmentation of the lung lobe is performed using marker based watershed algorithm. The proposed algorithm consists of five stages. The first stage is the segmentation of blood vessel using thresholding. The second step involves the segmentation of fissures based on the Eigen values of the Hessian matrix. The third stage is the segmentation of bronchial tree through region growing. Fusing these three results to form the cost image is the fourth step. The final stage is the application of watershed algorithm for the cost image. The run time, mean and accuracy calculation were performed and the results showed that the proposed method possess minimum runtime, mean distance when compared with the existing methodology. KEYWORDS— fissure, lung lobe, cost image, watershed transform, hessian matrix