Automatic tuberculosis screening using canny Edge detection method

Tuberculosis is a major health threat in many regions of the world. When left undiagnosed and consequently untreated, death rates of patients with tuberculosis are high. We first extract the lung region using a lung nodule Edge detection method. For this lung region, we compute a set of texture and shape features, which enable the x-rays to be classified as normal or abnormal using a binary classifier. Edge detection, especially step edge detection has been widely applied in various different computer vision systems, which is an important technique to extract useful structural information from different vision objects and dramatically reduce the amount of data to be processed. John has found that, the requirements for the application of edge detection on diverse vision systems are relatively the same. Thus, a development of edge detection solution to address these requirements can be implemented in a wide range of situations. The general criteria for edge detection includes detection of edge with low error rate, which means that the detection should accurately catch as many edges.