  Alzheimer's disease is the most common cause of dementia, yet difficult to
accurately diagnose without the use of invasive techniques, particularly at the
beginning of the disease. This work addresses the classification and analysis
of multispectral synthetic images composed by diffusion-weighted magnetic
resonance brain volumes for evaluation of the area of cerebrospinal fluid and
its correlation with the progression of Alzheimer's disease. A 1.5 T MR imaging
system was used to acquire all the images presented. The classification methods
are based on multilayer perceptrons and classifiers of radial basis function
networks. It is assumed that the classes of interest can be separated by
hyperquadrics. A polynomial network of degree 2 is used to classify the
original volumes, generating a ground-truth volume. The classification results
are used to improve the usual analysis by the map of apparent diffusion
coefficients.
