An intelligent system for segmenting lung image using parallel programming

Computed tomography is used nowadays for analyzing the problem in the human body and it plays a very important role in diagnosing defects in the patients. Computed tomography only became feasible with the development of computer signal processing capabilities. Technology is improved to capture the inner parts of the human body from 2D to 3D and also from 3D to 4D. A tomographic image is a cross sectional images or slices through the body. A radiologist has to analyze the slices one by one for detecting any defect, it takes long time when the number of slices is more and hence the time for doing the analysis was more. This paper presents a system which predicts the affected areas of human lungs from slices obtained from CT scan Machine, using parallel image processing and enhancing algorithms, to assist radiologists to make their final decisions. The proposed model was tested on the human lung for the detection of cancer. The scanned images are stored in the form of Digital Imaging and Communication in Medicine (DICOM).