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Automatic MRI&PET analysis for the Alzheimer's disease early diagnosis.

Structrural and functional neuroimaging are being used to asses the early detection of Alzheimer’s disease. The most clinically modalities used are magnetic resonance (MRI) and positron emission tomography (PET). The aim of this study is to present a robust and computationally e!cient method for unsupervised anatomical (MRI) and metabolic (FDG-PET) neuroimaging analysis, its goal is the early detection of Alzheimer’s disease. PETs are co-registered onto MRIs and a region based analysis is performed. The procedure takes a raw MR image, normalizes it to the ICBM152, and extracts two regions containing portion of the medial temporal lobe (MTL) around each hippocampus. Upon co-registration onto the MRI, the PET intensity is normalized to the cerebellum counts. The metabolic data are extracted from the same region sampled from the MRI. From these regions significant features are selected using random forest (RF) and finally the classification is done with RF and support vector machine (SVM), resulting in anatomical&metabolic sensitiviy/specificity 95% classification healty controls (HC) and Alzheimer disease patients (AD) and sensitivity/specificty 80% classification between HC and mild cognitive imparment patients (MCI). The present procedure allows unsupervised extraction and statistical analysis in a small volume centered around the hippocampus showing structural and functional abnormalities that enhanced the prediction ability on MCIs and improves discrimination among controls and AD subjects.