Analysis of spect brain images using Wilcoxon and relative entropy criteria and quadratic multivariate classifiers for the diagnosis of Alzheimer's disease

Authors:
F. J. Martínez;D. Salas-González;J. M. Górriz;J. Ramírez;C. G. Puntonet;M. Gómez-Río
Affiliations:
Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Computers Architecture and Technology;Virgen Nieves Hospital, Deptartment Nuclear Medicine, Granada, Spain
Venue:
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Year:
2011

Citing 3
Cited 1

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Principles of multivariate analysis: a user's perspective
SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information

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GMM based SPECT image classification for the diagnosis of Alzheimer's disease

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Association rule-based feature selection method for Alzheimer's disease diagnosis

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Abstract

This paper presents a computer aided diagnosis technique for improving the accuracy of the early diagnosis of the Alzheimer's disease. 97 SPECT brain images from the "Virgen de las Nieves" Hospital in Granada are studied. The proposed method is based on two different classifiers that use two different separability criteria and a posterior reduction of the feature dimension using factor analysis. Factor loadings are used as features of two multivariate classifiers with quadratic discriminant functions. The result of these two different classifiers is used to figure out the final decision. An accuracy rate up to 92.78% when NC and AD are considered is obtained using the proposed methodology.