IMAGE MINING EFFECT USING GAUSSIAN SMOOTH IN BRAIN TUMOR INCREASING THE SEGMENTING ACCURACY (I- MENINGIOMA)

The accurate and effective algorithm for segmenting image is very useful in many fields, especially in medical image. In this paper we introduced a novel method that focus on segmenting the brain MR Image that is important for neural diseases. Because of many noises embedded in the acquiring procedure, such as eddy currents, susceptibility artifacts, rigid body motion, and intensity inhomogeneity, segmenting the brain MR image is a difficult work. In this sushisen algorithm, we overcame the inhomogeneity shortage, by modifying the objective function with compensating its meningiomas immediate neighborhood effect using Gaussian smooth method for decreasing the influence of the inhomogeneity and increasing meningiomas the segmenting accuracy. With simulate image and the clinical MRI 2016 dataset, the experiments shown that our proposed algorithm is effective.