Automatic Segmentation of Hippocampus and Classification of brain MRI for Alzheimer’s Detection

Hippocampus is the structure of brain thatis mostly affected by Alzheimer's disease at an early stage. Atrophy of hippocampus has been found asa predictive feature for Alzheimer's disease diagnosis. To measure the atrophy of hippocampus we need to segment it out from surrounding structures of brain. Manual segmentation of hippocampus has beenfound standard technique for hippocampus segmentation in literature, but isvery time consuming and depends on particular anatomical information. In this work we have proposed an automatic approach to segment hippocampus considering texture and active contour from the brain Magnetic Resonance Image. After segmentation, features based on atrophy and shape of hippocampus has beenmeasured. Support vector machine classifier with radial basis function kernel has been analyzed with extracted features for classification of Alzheimer's and control subjects. In the proposed technique, 200AD MRI and 200control MRI have been considered from Alzheimer's Disease Neuroimaging Initiative database. The experiment have shown 93% accuracy, 0.96 sensitivity and 0.90specificity with atrophy feature and 94% accuracy, 0.96sensitivity and 0.92specificity with shape feature. Further, 0.96sensitivity, 1 specificity and 98% accuracyhave beenobtained with the fusion of atrophy and shape feature.