Functional Image Classification Techniques For Early Alzheimer’s Disease Detection
Pp. 107-122 (16)
I. Alvarez-Illan, Miriam M. Lopez, J. M. Gorriz, J. Ramirez, F. Segovia, D. Salas-Gonzalez, R. Chaves and C. G. Puntonet
Conventional evaluation of functional image 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. In this chapter, several techniques for feature extraction
and classification methods are presented as an automatic alternative to explore the images with
the aim of detecting the Alzheimer’s Disease (AD) in its early stage. The huge number of voxels
of a typical brain scan makes necessary to use data reduction and compression techniques as
well as other feature extraction methods that allow to hold the discriminant information in lower
dimensional feature vectors, solving that way the well-known small sample size problem. The
extracted features can be subsequently combined with different classification techniques to define
a complete Computer Aided Diagnosis (CAD) system capable to distinguish successfully between
normal controls and AD affected subjects.
Image classification, SPECT, PET, Support vector machines, Computer aided diagnosis system,
Dpt. Signal Theory Networking and Communications. ETSIIT-UGR, 18071, University of Granada, Spain.