Partial least squares for feature extraction of SPECT images

Authors:
F. Segovia;J. Ramírez;J. M. Górriz;R. Chaves;D. Salas-Gonzalez;M. López;I. Álvarez;P. Padilla;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 Signal Theory, Networking and Communications, University of Granada, Spain;Dept of Signal Theory, Networking and Communications, University of Granada, Spain;Dept of Computer Architecture and Computer Technology, University of Granada, Spain
Venue:
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Year:
2010

Citing 2
Cited 0

SVM Feature Selection for Classification of SPECT Images of Alzheimer's Disease Using Spatial Information

ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Effective Emission Tomography Image Reconstruction Algorithms for SPECT Data

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

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Abstract

Single Photon Emission Computed Tomography (SPECT) images are commonly used by physicians to assist the diagnosis of several diseases such as Alzheimer's disease (AD) The diagnosis process requires the visual evaluation of the image and usually entails time consuming and subjective steps In this context, computer aided diagnosis (CAD) systems are desired This work shows a complete CAD system that uses SPECT images for the automatic diagnosis of AD and combines of support vector machine (SVM) learning with a novel methodology for feature extraction based on the partial least squares (PLS) regression model This methodology avoids the well-known small sample size problem that multivariate approaches suffer and yields peak accuracy rates of 95.9% The results achieved are compared with the obtained ones by an PCA-based CAD system which is used as baseline.