DocumentCode :
2951535
Title :
Grouping a few sets of normally distributed voxels of SPECT volumes in discrimination between Alzheimer dementia and controls
Author :
Yin, Tang-Kai ; Chiu, Nan-Tsing ; Pai, Ming-Chyi
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Univ. of Kaohsiung, Kaohsiung, Taiwan
fYear :
2010
fDate :
Aug. 31 2010-Sept. 4 2010
Firstpage :
6126
Lastpage :
6129
Abstract :
It is widely accepted and can be easily verified that any specific voxel in a class of brain single photon emission computed tomography (SPECT) volumes is of a univariate normal distribution. In this research, we conjecture that all the voxels in a class of SPECT volumes are also approximately of a multivariate normal (MVN) distribution from which in terms of the Bayes errors of statistics, an optimal classifier can be designed using quadratic discriminant functions (QDFs). However, the number of training volumes needed for deriving the covariance matrix of an MVN distribution increases quadratically with respect to the number of voxels such that practically the MVN distributions cannot be modeled. To overcome this, we selected a reduced number of voxels and put them into groups based on the P values of two-sided t tests or a greedy algorithm of discrimination between two classes of volumes. We also tried the same approach on the 3D Haar wavelet coefficients which were obtained from the discrete wavelet transform of the voxels. Experiments showed that the accuracies of QDFs, linear discriminant functions (LDFs), and support vector machines (SVMs) were not significantly different in discrimination between Alzheimer´s and normal controls verifying that the proposed MVNs effectively model the discrimination information. Moreover, the proposed QDF classifier obtained satisfactory performance.
Keywords :
Bayes methods; Haar transforms; brain; covariance matrices; discrete wavelet transforms; diseases; error statistics; feature extraction; greedy algorithms; image classification; medical image processing; neurophysiology; single photon emission computed tomography; support vector machines; 3D Haar wavelet coefficients; Alzheimer dementia; Bayes statistic errors; brain; classifier; covariance matrix; feature extraction; greedy algorithm; linear discriminant functions; multivariate normal distribution; quadratic discriminant functions; single photon emission computed tomography; support vector machines; two-sided t tests; univariate normal distribution; wavelet transform; Accuracy; Dementia; Feature extraction; Greedy algorithms; Support vector machines; Three dimensional displays; Training; Aged; Algorithms; Alzheimer Disease; Case-Control Studies; Humans; Tomography, Emission-Computed, Single-Photon; Wavelet Analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE
Conference_Location :
Buenos Aires
ISSN :
1557-170X
Print_ISBN :
978-1-4244-4123-5
Type :
conf
DOI :
10.1109/IEMBS.2010.5627802
Filename :
5627802
Link To Document :
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