Preprocessing of 18F-DMFP-PET Data Based on Hidden Markov Random Fields and the Gaussian Distribution Segovia Román, Fermín Gorriz Sáez, Juan Manuel Ramírez Pérez De Inestrosa, Javier Martínez Murcia, Francisco Jesús Salas González, Diego PET image segmentation 18F-DMFP-PET data intensity normalization 18F-DMFP-PET is an emerging neuroimaging modality used to diagnose Parkinson’s disease (PD) that allows us to examine postsynaptic dopamine D2/3 receptors. Like other neuroimaging modalities used for PD diagnosis, most of the total intensity of 18F-DMFPPET images is concentrated in the striatum. However, other regions can also be useful for diagnostic purposes. An appropriate delimitation of the regions of interest contained in 18F-DMFP-PET data is crucial to improve the automatic diagnosis of PD. In this manuscript we propose a novel methodology to preprocess 18F-DMFP-PET data that improves the accuracy of computer aided diagnosis systems for PD. First, the data were segmented using an algorithm based on Hidden Markov Random Field. As a result, each neuroimage was divided into 4 maps according to the intensity and the neighborhood of the voxels. The maps were then individually normalized so that the shape of their histograms could be modeled by a Gaussian distribution with equal parameters for all the neuroimages. This approach was evaluated using a dataset with neuroimaging data from 87 parkinsonian patients. After these preprocessing steps, a Support Vector Machine classifier was used to separate idiopathic and non-idiopathic PD. Data preprocessed by the proposed method provided higher accuracy results than the ones preprocessed with previous approaches. 2024-11-21T12:52:32Z 2024-11-21T12:52:32Z 2017-10-09 journal article Segovia Román, F. et. al. Front. Aging Neurosci. 9:326. [https://doi.org/10.3389/fnagi.2017.00326] https://hdl.handle.net/10481/97227 10.3389/fnagi.2017.00326 eng info:eu-repo/grantAgreement/EC/MSC/2911780 http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Frontiers Media