A novel CAD system for local and global early diagnosis of Alzheimer's disease based on PIB-PET scans

This manuscript presents a Computer Aided Diagnosis (CAD) system to assist in the early diagnosis of Alzheimer's disease (AD) with the ability to provide a personalized diagnosis by visualizing the detected abnormality per brain regions (AAL atlas). The CAD system utilizes PiB-PET scans and consists of the following four essential stages-(1) a preprocessing step performs data reorientation, co-registration, and spatial normalization; (2) partitioning the brain into 116 labeled regions utilizing a brain atlas to facilitate local diagnosis; (3) extraction of features within each region using scale-invariant Laplacian of Gaussian (LoG) that detects the maximum or minimum of a radially symmetric intensity distribution; and (4) construction of two diagnosis layers (local and global) using a Support Vector Machine (SVM) classifier and its probabilistic variant (pSVM). The CAD system was tested on 84 PiB-PET scans (19 normal control (NC) and 65 mild cognitive impairment (MCI)) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The proposed system had a classification accuracy, sensitivity, and specificity of 100%.