Semi Quantitative Analysis and Classification of Alzheimer Disease using Positron Emission Tomography Images

Abstract

Alzheimer’s disease is an irreversible, degenerative neurological brain disease. It is marked by the buildup of plaque and tangles in the neurons of Alzheimer’s patients. A person with Alzheimer’s disease will have memory loss and mental functions are inhibited. Though there is no clear picture, but experts believe as many as 5.1 million Americans are currently suffering from Alzheimer’s (Alzheimer’s Fact Sheet) and proportionally there are Alzheimic patients. Although there is no cure for the disease, early detection of Alzheimer’s is crucial because it allows the patient to immediately begin a drug regimen that decrease the pace of the process of the disease. Biomarker tools for early diagnosis and disease progression in Alzheimer’s disease (AD) remain key issues in AD diagnosis .Efficient methods to identify early AD and to monitor the treatment effects in mild, moderate AD patients could revolutionize current trial practice. In modern medicine, PET imaging using fluorodeoxyglucose (FDG) is the most effective method of diagnosing AD. Brain changes in severe AD diseases are hypometabolism in frontal and temporal lobes of the brain, extreme shrinkage of cerebral cortex, severely enlarged ventricles. In this paper, we have proposed a novel methodology to do early detection using feature selection and classification algorithm that will address the above clinical problem.