Brain MR image classification for Alzheimer’s disease diagnosis using structural hippocampal asymmetrical attributes from directional 3-D log-Gabor filter responses

Abstract

Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative condition whose development is characterized by lateralized brain atrophies. In AD, the hippocampus is the first brain structure to present atrophy, which, although to a lesser extent, is also a precursor to the broader asymmetrical development of the human brain. Structural magnetic resonance (MR) imaging is capable of detecting the disease-induced anatomical changes in the brain, thus aiding the diagnosis of AD. MR image attributes extracted from the hippocampal regions are commonly used for the AD classification task. However, most of the published methods do not explore hippocampal asymmetries for image classification. In this study, we propose a new technique for performing the classification of MR images for AD using only hippocampal asymmetrical attributes. By using the new proposed asymmetry index (AI), we assessed the attributes and the ones that passed the analysis of variance test, i.e., showing statistically mean differences among the classes (CN, MCI, and AD), were selected for classification. As a result of our study, the statistical analysis of our AI has shown a significant increase in hippocampal asymmetry as disease progress (CN < MCI < AD). Moreover, for the classification using clinical MR images, we obtained accuracy values of 69.44% and 82.59%; and AUC values of 0.76 and 0.9 for CN × MCI and CN × AD, respectively. Last, we found the results of our asymmetry analysis consistent with other statistical assessments and our classification results, using only asymmetry attributes comparable to (or even higher than) existing hippocampus studies.