Partial least squares for feature extraction of SPECT images

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. © 2010 Springer-Verlag.