Extracting Regional Brain Patterns for Classification of Neurodegenerative Diseases

In structural Magnetic Resonance Imaging (MRI), neurodegenerative diseases generally present complex brain patterns that can be correlated with different clinical onsets of this pathologies. An objective method that aims to determine both global and local changes is not usually available in clinical practice, thus the interpretation of these images is strongly dependent on the radiologist's skills. In this paper, we propose a strategy which interprets the brain structure using a framework that highlights discriminant brain patterns for neurodegenerative diseases. This is accomplished by combining a probabilistic learning technique, which identifies and groups regions with similar visual features, with a visual saliency method that exposes relevant information within each region. The association of such patterns with a specific disease is herein evaluated in a classification task, using a dataset including 80 Alzheimer's disease (AD) patients and 76 healthy subjects (NC). Preliminary results show that the proposed method reaches a maximum classification accuracy of 81.39%.