Automatic Detection of Alzheimer Disease Based on the Area Analysis of White and Gray Matter Regions in Brain Images

By increasing life expectancy in the world, especially in developed countries, Alzheimer’s disease has become one of the most important and costly diseases. It is estimated that the prevalence of dementia will double every 5 years for people over 60 years. Unfortunately, this disease and other types of dementias impose a heavy burden on the economies of societies. In this study an automated method is presented and evaluated for Alzheimer disease detection in order to assist physician diagnosis. Methods and Materials: Magnetic resonance images of 236 patients were used. First, the gray and white matter regions and cerebrospinal fluid were extracted by image processing methods and their area were calculated and considered as feature vector for classification. Then, the support vector machine and neural networks were used to classify the Alzheimer patients from healthy ones. The gray matter region areas were significantly lower in Alzheimer group than healthy one. Using the regions area as feature vector and by means of support vector machine and neural networks as classifiers, a detection accuracy of 85% was achieved. Discussion and Alzheimer disease reduces the area of gray matter in brain which this changes are irreversible. Intellectual methods can detect this changes and diagnose Alzheimer disease. This methods could be used to assist physician for detection of disease.