Segmentation of MRI images to detect multiple sclerosis using non-parametric, non-uniform intensity normalization and support vector machine methods

Abstract

Multiple sclerosis (MS) is an inflammatory, chronic, persistent, and destructive disease of the central nervous system whose cause is not yet known but can most likely be the result of a series of unknown environmental factors reacting with sensitive genes. MRI is a method of neuroimaging studies that results in better image contrast in soft tissue. Due to the unknown cause of MS and the lack of definitive treatment, early diagnosis of this disease is important. MRI image segmentation is used to identify MS plaques. MRI images have an image error that is often called non-uniform light intensity. There are several ways to correct non-uniform images. One of these methods is Nonparametric Non-uniform intensity Normalization (N3). This method sharpens the histogram. The aim of this study is to reduce the effect of bias field on the MRI image using N3 algorithm and pixels of MRI images clustered by k-means algorithm. The dimensionality of the data is reduced by Principal Component Analysis (PCA) algorithm and then the segmentation is done by Support Vector Machine (SVM) algorithm. Results show that using the proposed system could diagnose multiple sclerosis with an average accuracy of 93.28%.