Journal
FRONTIERS IN AGING NEUROSCIENCE
Volume 9, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fnagi.2017.00326
Keywords
PET image segmentation; F-18-DMFP-PET data; intensity normalization; Hidden Markov Models; Gaussian distribution; Parkinson's disease
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Funding
- MINECO [TEC2012-34306, TEC2015-64718-R]
- Ministry of Economy, Innovation, Science and Employment of the Junta de Andalucia [P09-TIC-4530, P11-TIC-7103]
- European Union's Seventh Framework Program, Marie Sklodowska-Curie actions (COFUND) [291780]
- Ministry of Economy, Innovation, Science and Employment of the Junta de Andalucia
- Vicerectorate of Research and Knowledge Transfer of the University of Granada
- Salvador de Madariaga Mobility Grants
- Talent Hub project
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F-18-DMFP-PET is an emerging neuroimaging modality used to diagnose Parkinson's disease (PD) that allows us to examine postsynaptic dopamine D-2/3 receptors. Like other neuroimaging modalities used for PD diagnosis, most of the total intensity of F-18-DMFP-PET 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 F-18-DMFP-PET data is crucial to improve the automatic diagnosis of PD. In this manuscript we propose a novel methodology to preprocess F-18-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.
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