3 March 2017 Analysis of 18F-DMFP-PET data using Hidden Markov Random Field and the Gaussian distribution to assist the diagnosis of Parkinsonism
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Abstract
18F-DMFP-PET is a neuroimaging modality that allows us to analyze the striatal dopamine. Thus, it is recently emerging as an effective tool to assist the diagnosis of Parkinsonism and differentiate among parkinsonian syndromes. However the analysis of these data, which require specific preprocessing methods, is still poorly covered. In this work we demonstrate a novel methodology based on Hidden Markov Random Fields (HMRF) and the Gaussian distribution to preprocess 18F-DMFP-PET data. First, we performed a selection of voxels based on the analysis of the histogram in order to remove low-signal regions and regions outside the brain. Specifically, we modeled the histogram of intensities of a neuroimage with a mixture of two Gaussians and then, using a HMRF algorithm the voxels corresponding to the low-intensity Gaussian were discarded. This procedure is similar to the tissue segmentation usually applied to Magnetic Resonance Imaging data. Secondly, the intensity of the selected voxels was scaled so that the Gaussian that models the histogram for each neuroimage has same mean and standard deviation. This step made comparable the data from different patients, without removing the characteristic patterns of each patient's disorder. The proposed approach was evaluated using a computer system based on statistical classification that separated the neuroimages according to the parkinsonian variant they represented. The proposed approach achieved higher accuracy rates than standard approaches for voxel selection (based on atlases) and intensity normalization (based on the global mean).
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Fermín Segovia, Diego Salas-Gonzalez, Juan M. Górriz, Javier Ramírez, Francisco J. Martínez-Murcia, "Analysis of 18F-DMFP-PET data using Hidden Markov Random Field and the Gaussian distribution to assist the diagnosis of Parkinsonism", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101342B (3 March 2017); doi: 10.1117/12.2250281; https://doi.org/10.1117/12.2250281
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