Analysis of SPECT brain images for the diagnosis of Alzheimer's disease using moments and support vector machines.
This paper presents a computer-aided diagnosis technique for improving the accuracy of diagnosing the Alzheimer's type dementia. The proposed methodology is based on the calculation of the skewness for each m-by-m-by-m sliding block of the SPECT brain images. The center pixel in this m-by-m-by-m block is replaced by the skewness value to build a new 3-D brain image which is used for classification purposes. After that, voxels which present a Welch's t-statistic between classes, Normal and Alzheimer's images, higher (or lower) than a threshold are selected. The mean, standard deviation, skewness and kurtosis are calculated for these selected voxels and they are subjected as features to linear kernel based support vector machine classifier. The proposed methodology reaches accuracy higher than 99% in the classification task.
Salas-Gonzalez
Diego
D
Department of Signal Theory, Networking and Communications, ETSIIT 18071, University of Granada, Spain. dsalas@ugr.es
Górriz
Juan M
JM
Ramírez
Javier
J
López
Miriam
M
Illan
Ignacio A
IA
Segovia
Fermín
F
Puntonet
Carlos G
CG
Gómez-Río
Manuel
M
D000544:Alzheimer Disease / Q000000981:diagnostic imaging*; D001921:Brain / Q000000981:diagnostic imaging*; D003545:Cysteine / Q000031:analogs & derivatives; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D015609:Organotechnetium Compounds; D019275:Radiopharmaceuticals; D015899:Tomography, Emission-Computed, Single-Photon
D015609:Organotechnetium Compounds; D019275:Radiopharmaceuticals; C059659:technetium Tc 99m bicisate; D003545:Cysteine