Functional Brain Image Classification Techniques for Early Alzheimer Disease Diagnosis

DOI: 10.1007/978-3-642-02267-8_17

ABSTRACT Currently, the accurate diagnosis of the Alzheimer disease (AD) still remains a challenge in the clinical practice. As the
number of AD patients has increased, its early diagnosis has received more attention for both social and medical reasons.
Single photon emission computed tomography (SPECT), measuring the regional cerebral blood flow, enables the diagnosis even
before anatomic alterations can be observed by other imaging techniques. However, conventional evaluation of SPECT images
often relies on manual reorientation, visual reading and semiquantitative analysis of certain regions of the brain. This paper
evaluates different pattern classifiers including k-nearest neighbor (kNN), classification trees, support vector machines and feedforward neural networks in combination with template-based normalized
mean square error (NMSE) features of several coronal slices of interest (SOI) for the development of a computer aided diagnosis
(CAD) system for improving the early detection of the AD. The proposed system, yielding a 98.7% AD diagnosis accuracy, reports
clear improvements over existing techniques such as the voxel-as-features (VAF) which yields just a 78% classification accuracy.