Structural MRI - based classification of alzheimer's disease

ABSTRACT Alzheimer’s disease (AD), an irreversible neurodegenerative dementia, occurs most frequently in older adults which gradually destroys regions of the brain that are responsible for memory, learning, thinking and behavior. By estimation, 5.3 million Americans of all ages suffered from AD in 2015. This number is expected to increase to 16 million people by 2050. AD is the only cause of death in the top 10 of Americans that cannot be cured, prevented or slowed. Presently, there is no cure for AD, but early detection may help to figure out the root of AD mechanisms and improve the quality of life for patients who suffer from AD. In recent years, analysis of neuroimaging data has attracted a lot of interest with the recent improvements for early and accurate detection of AD. Neuroimaging techniques have become an important field of research due to the progress in their acquisition, storage and management in a wide range of applications including AD detection. High accurate image-based early detection of AD could provide valuable support for clinical treatments. High-dimensional classification methods have been a major target in the field of machine learning for the automatic AD detection. One major issue of automatic AD classification is the feature-selection method from high-dimensional feature space. This study proposes novel feature selection methods for high dimensional pattern recognition problem aimed at high accurate detection of AD, which uses the information from three dimensional magnetic resonance imaging (MRI) data extracted from the brain. MRI-based brain data used in the present study are obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). This work focuses on structural MRI data and investigates extraction and selection of features, which are the main blocks in an automatic diagnosis detection system. In this regard, Voxel-based-morphometry (VBM) analysis of cross-sectional 3-Tesla 3D T1-weighted MRI data is utilized to perform feature extraction. VBM is an automated technique for assessment of whole brain structure with voxel-by-voxel comparisons which has been developed to analyze tissue concentrations or volumes between subject groups to distinguish degenerative diseases with dementia. The significant local differences in gray matter volumes (gray matter atrophies) based on VBM analysis are selected as 3-D volumes of interests (VOIs). Feature extraction based on the 3D voxel clusters detected by VBM on structural MRI (sMRI) and voxel values of VOIs are considered as raw features. In the feature selection stage, novel methods based on probability distribution function (PDF) and feature ranking are introduced to select most discriminative features from high-dimensional data. In the PDF-based feature selection approach, a novel statistical feature-selection process is employed, utilizing the PDF of the VOI to represent statistical patterns of the respective high dimensional sMRI sample. PDF of the VOIs can be considered a lower-dimensional feature vector representing sMRI images. The dimensionality of the PDF-based feature vector can be adjusted by changing the number of bins of the PDF. In this regard, the Fisher Criterion is used to determine the optimal number of bins of the histogram generating the PDF. In the proposed feature ranking method, all raw features are ranked using seven different statistical measures methods, namely, statistical dependency (SD), mutual information (MI), information gain (IG), Pearson’s correlation coefficient (PCC), t-test score (TS), Fisher’s criterion (FC), and the Gini index (GI). These measures are indicators of class separability, therefore the features with higher scores are assumed to be more discriminative. Hence it is critical to determine the number of top features. In the current study, to determine the number of top features, two methods namely, Fisher criterion and classification error are introduced. The Fisher Criterion between AD and HC groups is calculated for all sizes of feature vectors, where the vector size maximizing Fisher Criterion is selected as the number of top discriminative features. In a similar spirit, the estimated classification error on training set made up of the AD and HC groups is calculated. The vector size that minimizing this error is selected as the size of the top discriminative feature vector. In the classification stage, the support vector machine (SVM) classifiers with linear and non-linear kernels are employed to perform binary classification using 10 fold cross validation between patients who suffer from AD and age-matched healthy controls. Moreover, data fusion techniques are proposed to achieve higher performance in AD detection. In this regard, data fusion is introduced to improve the classification performance, by combining scores or vectors received from clusters obtained from MRI images based on the severity of gray matter atrophy in the brain. In addition, a novel data fusion approach among feature ranking methods is introduced. The results indicate that proposed approaches are reliable techniques that are highly competitive with the state-of-the-art techniques in classification of AD. Keywords: Alzheimer’s disease, Structural MRI, Voxel-based morphometry, Statistical feature extraction, Probability distribution function, Feature ranking, Fisher Criterion, classification error, Data fusion, , Support vector machine.