Evaluation and classification of AD in magnetic resonance images on hippocampus region using support vector machines

The identification of several behavioral symptoms is often mistakenly associated to the age-related concerns or stress in Alzheimer's disease. The correct diagnosis and monitoring of the disease requires of additional resources. A new methodology for classification of Alzheimer's disease is from MR images for medical support. A large database with more than five hundred patient's images was used. Two different problems are tackled in this work: a first one where a classification method is developed to classify MR images as either normal or with the Alzheimer's disease and a second one for the identification and classification between normal subjects, MCI patients and AD patients. It is noteworthy we could offer a tool support vector machines to assist the early diagnosis of dementia. The outline of the methodology includes wavelet feature extraction from the MRIs, dimensionality reduction, training-test subdivision and classification using Support Vector Machines.