Selecting Regions of Interest for the Diagnosis of Alzheimer's Disease in Brain SPECT Images Using Welch's t-Test

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:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Year:
2009

Citing 3
Cited 1

An Automatic Threshold-Based Scaling Method for Enhancing the Usefulness of Tc-HMPAO SPECT in the Diagnosis of Alzheimer's Disease

MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information

Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Effective Emission Tomography Image Reconstruction Algorithms for SPECT Data

ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I

Early diagnosis of Alzheimer's disease based on Partial Least Squares and Support Vector Machine

Expert Systems with Applications: An International Journal

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Abstract

This paper presents a computer-aided diagnosis technique for the diagnosis of Alzheimer type dementia. The proposed methodology is based on the selection of voxels which present a Welch's t-test between both classes, Normal and Alzheimer images, greater than a given value. The mean and standard deviation of intensity values are calculated for selected voxels. They are chosen as feature vectors for two different classifiers: support vector machines with linear kernel and classification trees. The proposed methodology reaches an accuracy greater than 98 % in the classification task.