Lung Segmentation for Tuberculosis by Using Active

Tuberculosis is the extreme disease noted in the world. HIV/AIDS patients have an immune system which is impaired and that has made an impact on the problem. In the case of Tuberculosis the probability of mortality rate is not zero. In order to reduce the mortality rate the AAM (Active Appearance Model) in which training and segmentation process is undergone is used in the existing system. It is a method used for processing and analyzing manually traced segmentation examples during an automated training stage. Information about Image appearance and shape of lung structure is contained in a single model. But the major drawback is this method is not robust against occlusion. If the parts are occluded then they obtained result is unreliable. To overcome this problem we propose a Robust PCA (RPCA) method. By using this method computational effort is reduced and accurate reconstruction is also obtained. The results shows greater advantage for showing accuracy and speed for any distributed data.