Graph Cut Based Method for Automatic Lung Segmentation for Tuberculosis by using Screening Method in Chest Radiographs

In medical imaging technique tuberculosis is an important challenging approach. Most of the peoples affected by the tuberculosis and tuberculosis are a very big disease after the HIV in India. The mortality rate of the peoples is high by affecting tuberculosis. Chest radiographs are also called as chest x ray or CXR. By using graph cut segmentation method is used to extract the lung region and texture and shape features are classified by using binary classifier. The postero anterior is used to automatically detect the tuberculosis. The existing smear microscopy is slow and unreliable. The ROC curve is used to illustrate the performance of the binary classifier. Three terms are classified as follows: Lung segmentation, feature computation, classification. Automated nodule detection is more nature applications of decision support/automation for CXR and CT.