Abstract
Alzheimer’s disease is the most common cause of dementia, yet hard to diagnose precisely without invasive techniques, particularly at the onset of the disease. This work approaches image analysis and classification of synthetic multispectral images composed by diffusion-weighted magnetic resonance (MR) cerebral images for the evaluation of cerebrospinal fluid area and its correlation with the advance of Alzheimer’s disease. The MR images were acquired from an image system by a clinical 1.5 T tomographer. The classification methods are based on multilayer perceptrons, polynomial nets and Kohonen LVQ classifiers. The classification results are used to improve the usual analysis of the apparent diffusion coefficient map.
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dos Santos, W.P., de Souza, R.E., e Silva, A.F.D., Santos Filho, P.B. (2007). Evaluation of Alzheimer’s Disease by Analysis of MR Images Using Multilayer Perceptrons, Polynomial Nets and Kohonen LVQ Classifiers. In: Gagalowicz, A., Philips, W. (eds) Computer Vision/Computer Graphics Collaboration Techniques. MIRAGE 2007. Lecture Notes in Computer Science, vol 4418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71457-6_2
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DOI: https://doi.org/10.1007/978-3-540-71457-6_2
Publisher Name: Springer, Berlin, Heidelberg
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