Rough set theory based segmentation of tumor for MR images with CA approach

Population atlases provide an important prior to improve brain tumour segmentation by measuring the deviation from the normal brain. The application is in the clinical radio surgery planning, where manual segmentation of tumours are carried out on contrast enhanced T1-MR images by a radio-oncology expert, modify the cellular automata (CA) segmentation towards the nature of the tumour properties undergoing radiation therapy by adapting relevant transition rules. The proposed system is a novel semi supervised scheme with roughest theory for abnormality detection and segmentation in medical images. Semi supervised learning does not require pathology modelling and, thus, allows high degree of automation. Here formulates and maximize a strictly concave likelihood function estimating abnormality for each partition and fuse the local estimates into a globally optimal estimate that satisfies the consistency constraints, based on a distributed estimation algorithm.