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Evaluation of Alzheimer's Disease by Analysis of MR Images using Multilayer Perceptrons and Kohonen SOM Classifiers as an Alternative to the ADC Maps

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3 Author(s)
Wellington P. dos Santos ; Department of Electronics and Systems, Universidade Federal de Pernambuco, 50.740-530; Department of Computing Systems, Universidade de Pernambuco, 50.720-001, Recife, Pernambuco, Brazil. e-mail:, ; Ricardo E. de Souza ; Plinio B. dos Santos Filho

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 measuring the advance of Alzheimer's disease. A clinical 1.5 T MR imaging system was used to acquire all images presented. The classification methods are based on multilayer perceptrons and Kohonen self-organized map classifiers. We assume the classes of interest can be separated by hyperquadrics. Therefore, a 2-degree polynomial network is used to classify the original image, generating the ground truth image. The classification results are used to improve the usual analysis of the apparent diffusion coefficient map.

Published in:

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Date of Conference:

22-26 Aug. 2007