RELIABILITY ANALYSIS OF ANOMALIES AND DISEASE PREDICTION USING NOVEL OPTIMAL SEGMENTATION FRAMEWORK FOR VARIOUS NON – EQUILIBRIUM BRAIN TISSUE ANGULAR MOMENTUM

With the growing need for clinical diagnosis accuracy, the need for automation or computer aided analysis is also growing. Moderate numbers of methods are introduced to analyse the biological symptoms and produce the report to be recognized by the trained professions. Conversely, the final analysis is prone to errors due because of human interpretations. Also the computer aided reports leave huge scope for multiple further diagnoses. Thus the need of a novel algorithm for predictive analysis of diseases for brain disorder is much expected. This paper presents a fully reliable brain disease detection mechanism based on an enhancement in accuracy of multilateral filter and applied watershed method with EM-GM method. The proposed optimal unification method is timely and optimal methods to process the optimal sets of segments are divided and finest merged results. The multilateral filter enhances the image edges for better segmentation using signal amplitude moderation of the pixel. The final outcome of this paper produces the brain regions detected with anomalies and possible diseases, thus the number of possible further medical investigations are reduced.