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Alzheimer disease (AD) is a progressive neurodegenerative disorder first affecting memory functions and then gradually affecting all cognitive functions with behavioral impairments. As the number of patients with AD has increased, early diagnosis has received more attention for both social and medical reasons. Functional brain imaging including single-photon emission computed tomography (SPECT) is commonly used to guide the clinician΄s diagnosis. Conventional evaluation of SPECT scans often relies on manual reorientation, visual reading and semiquantitative analysis of certain regions of the brain. These steps are time consuming, subjective and prone to error. Currently, accuracy in the early diagnosis of certain neurodegenerative diseases such as the Alzheimer type dementia is below 70% and, frequently, these do not receive the suitable treatment. This paper shows a fully automatic computer-aided diagnosis (CAD) system for improving the accuracy in the early diagnosis of the AD. The proposed approach is based on a feature extraction process based on the normalized mean square error (NMSE) features of several coronal slices of interest (SOI) and support vector machine (SVM) classification. The proposed system yields clear improvements over existing techniques such as the voxel as features (VAF) approach yielding a 97.5% AD diagnosis accuracy. [ABSTRACT FROM AUTHOR]

Alzheimer΄s Disease (AD) is a progressive neurologic disease of the brain that leads to the irreversible loss of neurons and dementia. The new brain imaging techniques PET (Positron Emission Tomography) and SPECT (Single Photon Emission Computed Tomography) provide functional information about the brain activity and have been widely used in the AD diagnosis process. However, the diagnosis currently relies on manual image reorientations, visual evaluation and other subjective, time consuming steps. In this work, a complete computer aided diagnosis (CAD) system is developed to assist the clinicians in the AD diagnosis process. It is based on bayesian classifiers, made up from features previously extracted. The small size sample problem, consisting of having a number of available samples much lower than the dimension of the feature space, is faced up by applying Principal Component Analysis (PCA) to the features. This approach provides higher accuracy values than other previous approaches do, yielding 91.21% and 98.33% accuracy values for SPECT and PET images, respectively. [ABSTRACT FROM AUTHOR]