Segmentation and location of abnormality in brain MR images using distributed estimation

Abstract

This paper presents a modern semi supervised scheme for the detection and segmentation of abnormalities present in the brain MR images. The high degree of automation can be attained by using semi supervised learning, because it does not require any pathology modeling. If the dimensionality of the data is large then the estimation of the probability density function is not possible. To overcome this every image is handled as a network of locally coherent image partitions. Median filter is used for preserving edges while removing noise. Contrast enhancement automatically adjusts the intensity values of the image to achieve a better quality. The block wise separation is carried out by calculating the parameter like principal component analysis (PCA), Eigen value, Eigen vector, maximum likelihood function. The maximum likelihood function which estimating the abnormality for each partition is formulated. The likelihood function consists of a model and a data term and is formulated as a quadratic programming problem.