19 November 2013 Extracting regional brain patterns for classification of neurodegenerative diseases
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Proceedings Volume 8922, IX International Seminar on Medical Information Processing and Analysis; 892208 (2013) https://doi.org/10.1117/12.2035515
Event: IX International Seminar on Medical Information Processing and Analysis, 2013, Mexico City, Mexico
Abstract
In structural Magnetic Resonance Imaging (MRI), neurodegenerative diseases generally present complex brain patterns that can be correlated with di erent 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 identi es 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 speci c disease is herein evaluated in a classi cation 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 classi cation accuracy of 81.39%.
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Andrea Pulido, Andrea Rueda, Eduardo Romero, "Extracting regional brain patterns for classification of neurodegenerative diseases", Proc. SPIE 8922, IX International Seminar on Medical Information Processing and Analysis, 892208 (19 November 2013); doi: 10.1117/12.2035515; https://doi.org/10.1117/12.2035515
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