Automatic System for Alzheimer's Disease Diagnosis Using Eigenbrains and Bayesian Classification Rules

Authors:
M. López;J. Ramírez;J. M. Górriz;I. Álvarez;D. Salas-Gonzalez;F. Segovia;C. G. Puntonet
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 Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Architecture and Computer Technology, University of Granada, Spain
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

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
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
Eigenfaces for recognition

Journal of Cognitive Neuroscience

Exploratory matrix factorization for PET image analysis

HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I

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Abstract

Alzheimer's Disease (AD) is a progressive neurologic disease of the brain that leads to the irreversible loss of neurons and dementia. The new brain imaging techniques PET (Positron Emission Tomography) and SPECT (Single Photon Emission Computed Tomography) provide functional information about the brain activity and have been widely used in the AD diagnosis process. However, the diagnosis currently relies on manual image reorientations, visual evaluation and other subjective, time consuming steps. In this work, a complete computer aided diagnosis (CAD) system is developed to assist the clinicians in the AD diagnosis process. It is based on bayesian classifiers, made up from features previously extracted. The small size sample problem, consisting of having a number of available samples much lower than the dimension of the feature space, is faced up by applying Principal Component Analysis (PCA) to the features. This approach provides higher accuracy values than other previous approaches do, yielding 91.21% and 98.33% accuracy values for SPECT and PET images, respectively.