@misc{10481/97227,
year = {2017},
month = {10},
url = {https://hdl.handle.net/10481/97227},
abstract = {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.},
organization = {MINECO under the TEC2012-
34306 and TEC2015-64718-R projects},
organization = {Ministry of
Economy, Innovation, Science and Employment of the Junta
de Andalucía under the Excellence Projects P09-TIC-4530},
organization = {P11-TIC-7103},
organization = {Talent Hub project approved by the
Andalucía Talent Hub Program},
organization = {European Union’s
Seventh Framework Program, Marie Sklodowska-Curie actions
(COFUND — Grant Agreement no. 291780},
organization = {Ministry
of Economy, Innovation, Science and Employment of the Junta
de Andalucía},
publisher = {Frontiers Media},
keywords = {PET image segmentation},
keywords = {18F-DMFP-PET data},
keywords = {intensity normalization},
title = {Preprocessing of 18F-DMFP-PET Data Based on Hidden Markov Random Fields and the Gaussian Distribution},
doi = {10.3389/fnagi.2017.00326},
author = {Segovia Román, Fermín and Gorriz Sáez, Juan Manuel and Ramírez Pérez De Inestrosa, Javier and Martínez Murcia, Francisco Jesús and Salas González, Diego},
}