Automatic Pulmonary Cancer Detection using Prewitt & Morphological Dilation

Lung cancer is an uncontrollable growth of cell which starts from lung and later spread to the other part of the body, which is extremely perilous for the human life. Due to lung cancer several people reach towards crises every year. There are many medical imaging techniques that create visual pictures of inside the human body for diagnosis. A computed tomography is a type of medical imaging technique which allows to look inside the body but nevertheless it has been identified manually by radiologist. Manually cancer detection has high possibility human error due to similar gray levels that confuse the radiologist and the physicians, for eliminating this problem, it is required to develop an automated system. Hence the aim of this paper is early detection of lung cancer with the help of automated process for reducing human error and minimize false alarm rate and provide the process which is more accurate and effortless. In the proposed work automatic lung cancer detection takes place through Prewitt and morphological operations that provide better level of accuracy. Prewitt is a gradient based edge detection technique and after applying edge detection technique; dilation is to be applied for obtaining more visible image than the original. The obtained result has less false alarm rate with having fewer processing time along with high level of precision rate.