Abstract
Computer aided diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer’s Disease (AD), which, in turn, allows the application of treatments that can be simpler and more likely to be effective. This paper presents a straightfoward approach to determine the most discrimanative brain regions, defined by the Automated Anatomical Labelling (AAL), based on the measurements of the tissue density at the different brain areas. Statistical analysis of GM and WM densities reveal significant differences between controls (CN) and AD at specific brain areas associated to tissue density diminishing due to neurodegeneration. The proposed method has been evaluated using a large dataset from the Alzheimer’s disease Neuroimaging Initiative (ADNI). Classification results assessed by cross-validation proved that computed WM/GM densities are discriminative enough to differentiate between CN/AD. Moreover, fusing density measurements with ApoE genetic information help to increase the diagnosis accuracy.
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Ortiz, A. et al. (2015). Automated Diagnosis of Alzheimer’s Disease by Integrating Genetic Biomarkers and Tissue Density Information. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Artificial Computation in Biology and Medicine. IWINAC 2015. Lecture Notes in Computer Science(), vol 9107. Springer, Cham. https://doi.org/10.1007/978-3-319-18914-7_1
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DOI: https://doi.org/10.1007/978-3-319-18914-7_1
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