Linear intensity normalization of FP-CIT SPECT brain images using the α-stable distribution Diego Salas-Gonzalez a , Juan M. Górriz a, ⁎, Javier Ramírez a , Ignacio A. Illán a , Elmar W. Lang b a Dpt. Signal Theory, Networking and Communications, University of Granada, Spain b Computational Intelligence and Machine Learning Group, University of Regensburg, Germany abstract article info Article history: Accepted 1 October 2012 Available online 11 October 2012 In this work, a linear procedure to perform the intensity normalization of FP-CIT SPECT brain images is presented. This proposed methodology is based on the fact that the histogram of intensity values can be fitted accurately using a positive skewed α-stable distribution. Then, the predicted α-stable parameters and the location-scale property are used to linearly transform the intensity values in each voxel. This transformation is performed such that the new histograms in each image have a pre-specified α-stable distribution with de- sired location and dispersion values. The proposed methodology is compared with a similar approach assum- ing Gaussian distribution and the widely used specific-to-nonspecific ratio. In this work, we show that the linear normalization method using the α-stable distribution outperforms those existing methods. © 2012 Elsevier Inc. All rights reserved. Introduction Iodine-123-fluoropropyl-carbomethoxy-3-β-(4-iodophenyltropane) (Fazio et al., 2011; Neumeyer et al., 1991) (FP-CIT; 123 I-ioflupane/ DaTSCAN) has been used to differentiate between Parkinsonian syn- drome and essential tremors (Benamer et al., 2000; Marek et al., 2001; Seibyl et al., 1995). In addition, its importance has increased more recently when its application range was extended to be used for the differentiation of dementia with Lewy bodies from Alzheimer's disease (Colloby et al., 2004; Colloby et al., 2008; O'Brien et al., 2009; Walker et al., 2007). After intravenous injection, 123 I-FP-CIT binds to the dopamine transporters in the striatum. It has been found that patients with PD will exhibit decreased uptake of the tracer (Booij et al., 1997a, 1997b, 1998; Winogrodzka et al., 2001). Imaging with a gamma camera in single photon emission computed tomography (SPECT) mode allows visualization of the transporter distribution. Previous studies have demonstrated that when [ 123 I]β-CIT reaches equilibrium binding in the brain, a simple unitless ratio of regional radioactivities is proportional to the binding potential (Laruelle et al., 1994; Scherfler et al., 2005; Van Dyck et al., 1995). Furthermore, specific binding regions (putamen and caudate nuclei) appear more intense in healthy subjects than in PD subjects. Thus, this difference is usually quantified by the so-called binding potential or specific/nonspecific binding ratio (BR). BR VOI ¼ C VOI -C NSB C NSB ð1Þ where C VOI is the count per voxel in the volume of interest and C NSB denotes the mean count per voxel in the non specific binding region and it is widely used in the literature for normalization purposes in 123 I-FP-CIT SPECT images and also in other functional brain image modalities (Aarts et al., 2012; Andringa et al., 2005; Caretti et al., 2008; Isaias et al., 2006; Rektorova et al., 2008; Sharma and Ebadi, 2008; Zanotti-Fregonara et al., 2008). The occipital cortex is usually selected as the background region because the density of dopamine transporters is negligible in this brain area. In this work, in addition to the occipital cortex, we also consider the whole brain, except the voxel information in the striatum, as nonspecific brain region for comparison purposes. Furthermore, as Eq. (1) can be written as BR VOI ¼ CVOI CNSB -1, therefore, from now on, in this document we use the following equivalent expression for the binding ratio: BR VOI ¼ C VOI C NSB : ð2Þ Thus, the bulk of the normalized histogram of intensity values will be placed near 1 instead of 0. In addition, all the normalized values will be positive. We present a method of automatic intensity normalization of FP-CIT SPECT images. This proposed methodology takes advantage NeuroImage 65 (2013) 449–455 ⁎ Corresponding author. 1053-8119/$ – see front matter © 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.neuroimage.2012.10.005 Contents lists available at SciVerse ScienceDirect NeuroImage journal homepage: www.elsevier.com/locate/ynimg