Towards earlier detection of Alzheimer's disease using magnetic resonance images

Abstract

Alzheimer's disease (AD) is the most common type of dementia and a major cause of disability worldwide. Early detection of AD is essential to provide the patients with adequate and timely treatments and to help researchers monitor their effectiveness. Structural Magnetic Resonance Imaging (MRI) is a diagnostic tool that provides high-resolution images and a high brain tissue contrast.MRI-based biomarkers have been investigated in an attempt to describe and quantify structural differences between groups of normal elderly controls and subjects suffering from AD. Additionally, classification methods have been proposed that use these biomarkers as features to distinguish between those groups, thereby also providing diagnostic value.Two main approaches have been extensively explored in the past decades to perform early-stage AD classification based on structural MR images. The first uses the volume and/or the shape of specific brain structures, such as the hippocampi and the entorhinal cortex. As a consequence, these methods rely substantially on the quality of: 1) the assumptions of which brain regions are affected at an early stage of AD; 2) the segmentation of these brain structures, which suffers from large variability across studies. Another major line of research overcomes the first drawback by using voxelwise measures, such as the probability maps of the brain tissues. However, these methods require a voxelwise inter-subject correspondence, which is difficult to achieve, particularly considering the large anatomical variability of the brain across different subjects.Besides the above-mentioned disadvantages of these two approaches, they both focus on structural (volume, shape, density) changes only. It has recently been considered that also the MR image intensities and textures can provide complementary information that is overlooked by the structural-based features.In this thesis, we propose methods to help diagnose AD at an early stage of development. In particular, we build on the existing literature on classification approaches that use MR image textures for early detection of AD.vi Firstly, we focus our analysis on a type of lesions in the white matter (white matter hyperintensities) that have been shown to play a role in cognitive decline. We propose a method to automatically segment these lesions from a single MRI modality that can be suitable for large-scale clinical trials. We show that our method, despite using less information, performs similarly to current state-of-the-art multimodal approaches. Afterwards, we evaluate the performance of white matter lesion texture descriptors in the detection of Mild Cognitive Impairment (MCI, a transitional stage between normal ageing and dementia). Results show that the textures are more discriminative than the widely used lesion volumes and locations.Secondly, we evaluate three approaches that use texture descriptors without requiring prior brain structure segmentations. The first one takes the graylevel histograms computed in the whole brain and in sev...