Applied Soft Computing 11 (2011) 2313–2325 Contents lists available at ScienceDirect Applied Soft Computing journal homepage: www.elsevier.com/locate/asoc GMM based SPECT image classification for the diagnosis of Alzheimer’s disease J.M. Górriz a,∗ , F. Segovia a , J. Ramírez a , A. Lassl a,b , D. Salas-Gonzalez a a Departamento Teoría de la Se˜ nal, Telemática y Comunicaciones, Universidad Granada, Spain b Department Computational Intelligence and Machine Learning, Universität Regensburg, Germany article info Article history: Received 6 January 2010 Accepted 2 August 2010 Available online 11 August 2010 PACS: 87.19.xr 87.57.nm 87.57.R- 87.57.uh Keywords: SPECT Alzheimer’s disease Gaussian mixture model EM algorithm Support vector machines (SVMs) abstract We present a novel classification method of SPECT images based on Gaussian mixture models (GMM) for the diagnosis of Alzheimer’s disease. The aims of the model-based approach for density estimation is to automatically select regions of interest (ROIs) and to effectively reduce the dimensionality of the problem. The resulting Gaussians are constructed according to a maximum likelihood criterion employing the Expectation Maximization (EM) algorithm. By considering only the intensity levels inside the Gaussians, the resulting feature space has a significantly reduced dimensionality with respect to former approaches using the voxel intensities directly as features (VAF). With this feature extraction method one relieves the effects of the so-called small sample size problem and nonlinear classifiers may be used to distinguish between the brain images of normal and Alzheimer patients. Our results show that for various classifiers the GMM-based method yields higher accuracy rates than the classification considering all voxel values. © 2010 Elsevier B.V. All rights reserved. 1. Introduction Single Photon Emission Computed Tomography (SPECT) is a widely used technique to study the functional properties of the brain [1,2]. After the reconstruction and a proper normalization of the SPECT raw data, taken with Tc-99m ethyl cysteinate dimer (ECD) as a tracer, one obtains an activation map displaying the local intensity of the regional cerebral blood flow (rCBF). Therefore, this technique is particularly applicable for the diagnosis of neuro- degenerative diseases like for instance Alzheimer’s disease (AD) [3–9]. This functional modality has lower resolution and higher variability than others such as positron emission tomography (PET), but the use of SPECT tracers is relatively cheap, and the longer half- lives as compared to PET tracers make SPECT well suited, if not required, when biologically active radiopharmaceuticals have slow kinetics. SPECT modality also eliminates the need for an expen- sive on-site cyclotron/radiochemistry production facility typically required for the use of PET tracers thus, the former is very popular and is used in clinical practice nowadays. In order to improve the prediction accuracy especially in the early stage of the disease, where the patient could benefit most ∗ Corresponding author. E-mail address: gorriz@ugr.es (J.M. Górriz). from drugs, computer aided diagnosis (CAD) tools are desirable. At this stage in the development of CADs systems, the main goal is to reproduce the knowledge of medical experts in the evaluation of a complete image database, i.e. distinguishing AD patients from normal controls, thus errors from single observer evaluation are avoided along with the achievement of a method for assisting the identification of early signs of AD. 1.1. Background in CAD systems In this sense, several approaches for a computer aided diagnosis (CAD) system have been proposed in order to analyze SPECT and other medical images. The most relevant univariate analysis based approach to date is the widely used Statistical Parametric Mapping (SPM) and its numerous variants [10]. SPM consists of doing a vox- elwise statistical test, i.e. a two sample t-test, comparing the values of the image under study to the mean values of the group of normal images. Subsequently the significant voxels are inferred by using random field theory [11]. Its framework was first developed for the analysis of SPECT and PET studies, but it is now mainly used for the analysis of functional MRI (magnetic resonance imaging) data. However, SPM is not intended for the diagnosis problem using a single patient image but for comparing a group of images. Its appli- cation to this problem reports poor classification results since one of populations under study consist of a single test patient (biased 1568-4946/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.asoc.2010.08.012