Cancer Detection using Cellular Automata based Segmentation Techniques

Nowadays breast cancer, brain tumor, lung cancer are severe diseases to human being and it may cause a life risk. These can be cured also if it can be diagnosed at proper time. In this paper we have proposed one automated system that can detect brain tumor, breast tumor and lung cancer from different medical image modalities like X-ray or Computed Tomography (CT). It will help to identify the area of abnormalities where the mass is being developed. First, the quality of X-ray or CT image will be enhanced. Next Cellular Automata based segmentation will be applied to the growth of mass in brain or breast will be separated from its background. Cellular Automata (CA) is very much efficient technique for Bio-medical images in terms of computation time and clarity. In this proposed algorithm Moore neighborhood concept has been used. Inversion of the transformed image and image binarization help to get the segmented image in terms of identifying the abnormal growth of cells in brain, breast or in lungs. Henceforth, applying CA Segmentation Rules here the growth of mass has been detected and segmented from its background. This proposed methodology will help the physicians(medical) to identify the region of the abnormal cells in brain or breast or in lungs.