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.