Lung Cancer Detection Using Image Processing

The objective of this paper is to explore an expedient image segmentation algorithm for medical images to curtail the physicians’ interpretation of computer tomography (CT) scan images. Modern medical imaging modalities generate large images that are extremely grim to analyze manually. The consequences of segmentation algorithms rely on the exactitude and convergence time. At this moment, there is a compelling necessity to explore and implement new evolutionary algorithms to solve the problems associated with medical image segmentation. Nowadays, Image processing methods are commonly used in many medical areas for improvement of image for earlier detection and treatment stages. Early prediction of lung cancer can increase the survival rate of patient by using imaging tests such as computer tomography (CT) which gives better image quality and results. In Image processing procedures, methods like pre-processing, segmentation methods like thresholding or K-means clustering and feature extraction are discussed. It is the target to get more accurate results by using image enhancement and segmentation techniques on MATLAB software.