Classification of Multiple Sclerosis Disease using Cumulative Histogram

Menna Safwat,Fahmi Khalifa,Hossam El-Din

doi:10.14569/ijacsa.2020.0110634

Abstract

Multiple sclerosis (MS) is a chronic disease that affects different body parts including the brain. Detection and classification of MS brain lesions is of immense importance to physicians for the administration of appropriate treatment. Thus, this study investigates an automated framework for the diagnoses and classification of MS lesions in brain using magnetic resonance imaging (MRI). First, the MRI images format converted from dicom images of each patient into TIF format as MS lesion appears in white matter (WM) obviously. This is followed by a brain tissue segmentation using a k-nearest neighbor classifier. Then, cumulative empirical distributions or cumulative histograms (CH) of the segmented lesions are estimated along with other texture/statistical features that work on the difference between the intensity of MS lesions and its surrounding tissues. Finally, these CDFs are fused with and the statistical features for the classification of MS using K mean classifiers. Experiments are conducted, using transverse T2-weighted MR brain scans from 20 patients that are highly sensitive in detecting MS plaques, with gold standard classification obtained by an experienced MS. By comparing the evaluated performance with statistical features, our proposed fusion scored the highest accuracy with 98% and a false-positive rate of 1%.

Highlights

Multiple sclerosis (MS) is an autoimmune inflammatory chronic disease of the central nervous that appears in the white matter (WM) [1] [2]
This makes the automatic segmentation of MS lesions a challenging problem, so in [4] they focused on differentiating between active MS and cold-spot lesion from brain Magnetic resonance imaging (MRI)
A segmentation process is utilized using k-nearest neighbours (KNN) algorithm to segment the brain into three tissue parts: WM, the grey matter (GM) and MS lesion for each slice

Summary

Introduction

Multiple sclerosis (MS) is an autoimmune inflammatory chronic disease of the central nervous that appears in the white matter (WM) [1] [2]. MS is an illness that can influence on the optic nerves in your eyes, brain and spinal cord. The research work included in this thesis aims at creating a robust technique for the automatic segmentation of MR brain damage. This makes the automatic segmentation of MS lesions a challenging problem, so in [4] they focused on differentiating between active MS and cold-spot lesion from brain MRI. The challenge was in identification of MS in MR Images since the lesions have different size, shape and different locations with anatomical variability [6]

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