Analysis of Brain SPECT Images for the Diagnosis of Alzheimer Disease Using First and Second Order Moments

Authors:
D. Salas-Gonzalez;J. M. Górriz;J. Ramírez;M. López;I. Álvarez;F. Segovia;C. G. Puntonet
Affiliations:
Dept. of Signal Theory, Networking and Communications, University of Granada, Granada, Spain 18071;Dept. of Signal Theory, Networking and Communications, University of Granada, Granada, Spain 18071;Dept. of Signal Theory, Networking and Communications, University of Granada, Granada, Spain 18071;Dept. of Signal Theory, Networking and Communications, University of Granada, Granada, Spain 18071;Dept. of Signal Theory, Networking and Communications, University of Granada, Granada, Spain 18071;Dept. of Signal Theory, Networking and Communications, University of Granada, Granada, Spain 18071;Dept. of Computer Architecture and Computer Technology, University of Granada, Granada, Spain 18071
Venue:
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
Year:
2009

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Abstract

This paper presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of the Alzheimer type dementia. The proposed methodology is based on the selection of the voxels which present greater overall difference between both modalities (normal and Alzheimer) and also lower dispersion. We measure the dispersion of the intensity values for normals and Alzheimer images by mean of the standard deviation images. The mean value of the intensities of selected voxels is used as feature for different classifiers, including support vector machines with linear kernels, fitting a multivariate normal density to each group and the k-nearest neighbors algorithm. The proposed methodology reaches an accuracy of 92 % in the classification task.