Parkinson’s Disease Detection Using Structural MRI

Parkinson’s Disease (PD) is the second most encountered neurodegenerative disorder, second only to Alzheimer’s Disease (AD), and the most common movement disorder affecting 1% of people over the age of 60. PD is characterized by progressive loss of muscle control that causes trembling of the limbs and head at rest position, rigidity, slowness, impaired balance, and later on a shuffling gait. As the disease is progressed, difficulties in walking, talking, and completing basic tasks might occur. The causes of PD are unknown, yet it is believed that both the environmental and genetic factors might lead to PD. High-quality images obtained using neuroimaging methods could give beneficial support to the clinicians for evaluating the treatments. Threedimensional magnetic resonance imaging (3D-MRI) has been effectively utilized in the detection of progressive neurodegenerative diseases including PD. Therefore, using neuroimaging techniques with Computer-Aided Diagnosis (CAD) has gained increasing attention in the early and accurate diagnosis of PD. In this thesis, the extensive reviews on the studies of PD detection using MRI data and CAD methods since 2008 are studied. Furthermore, the affected brain regions owing to PD are obtained by using the 3D Volume of Interests (VOIs) and the captured affected brain regions are compared with the regions reported in the-state-of-the-art studies in the last decade. The obtained affected brain regions might shed light on the existing literature on PD diagnosis. In order to build an automatic method for PD detection, machine learning algorithms are applied to the processed neuroimaging data. It is obvious that pre-processing of 3D-MRI scans plays an important role for post-processing. In this thesis, the preprocessing of 3D-MRI data has been performed by using a Voxel-Based Morphometry (VBM) technique which evaluates the whole brain morphology with voxel-by-voxel comparisons. In VBM, some parameters such as covariates need to be defined to build a model for Gray Matter (GM) and White Matter (WM) volumes of Structural MRI (sMRI) datasets. In this thesis, the effects of using different covariates (i.e. total intracranial volume, age, sex and combination of them) on the classification of PD groups from Healthy Controls (HCs) have been studied. Additionally, in order to determine the 3D VOIs, the significant local alterations in GM and WM volumes of PD groups and HCs, a hypothesis either f-contrast or t-contrast need to be defined. In this thesis, the effects of two different hypotheses on PD detection have been investigated. Furthermore, a feature-level fusion technique in which the 3D GM and WM VOIs are combined considering the effects of both GM and WM volumes in PD diagnosis. The voxels extracted from 3D GM, WM, and the combination of GM and WM VOIs are considered as raw features. Even though the feature extraction decreases the number of features in raw data, using an automatic feature selection method from high dimensional feature space is an asset in PD classification. In this thesis, to select the most discriminative attributes from high-dimensional data, all raw features are ranked by using various feature ranking methods such as minimum redundancy maximum relevance, Relief-F, unsupervised feature selection for multi-cluster data, Laplacian score, regularized discriminative feature selection for unsupervised learning, correlation- based feature selection, and feature selection and kernel learning for local learning-based clustering. In order to select the optimal number of top-ranked discriminative features, a Fisher Criterion (FC) is calculated for different sizes of feature vectors and the optimal number of top-ranked features is selected when the vector size maximizes the FC. In order to classify the PD and HC, five different classification algorithms, namely k- nearest neighbor, naive Bayes, ensemble bagged trees, ensemble subspace discriminant, and support vector machines are used. Moreover, a decision fusion technique which combines the binary outputs of all five classifiers by using a majority voting method is investigated to achieve higher performance in PD diagnosis. The experimental results indicate that the proposed methods are reliable approaches that are highly competitive with the state-of-the-art methods in PD classification. Keywords: Parkinson’s disease, structural MRI, covariates, f-contrast, voxel-based morphometry.