dc.contributor.author | Brahim, A. | es_ES |
dc.contributor.author | Ramírez Pérez De Inestrosa, Javier | es_ES |
dc.contributor.author | Gorriz Sáez, Juan Manuel | es_ES |
dc.contributor.author | Khedher, Laila | es_ES |
dc.contributor.author | Salas-González, Diego | es_ES |
dc.date.accessioned | 2015-07-16T10:57:36Z | |
dc.date.available | 2015-07-16T10:57:36Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | Brahim, A.; et al. Comparison between Different Intensity Normalization Methods in 123I-Ioflupane Imaging for the Automatic Detection of Parkinsonism. Plos One, 10(6): e0130274 (2015). [http://hdl.handle.net/10481/36991] | es_ES |
dc.identifier.issn | 1932-6203 | |
dc.identifier.uri | http://hdl.handle.net/10481/36991 | |
dc.description.abstract | Intensity normalization is an important pre-processing step in the study and analysis of DaTSCAN SPECT imaging. As most automatic supervised image segmentation and classification methods base their assumptions regarding the intensity distributions on a standardized intensity range, intensity normalization takes on a very significant role. In this work, a comparison between different novel intensity normalization methods is presented. These proposed methodologies are based on Gaussian Mixture Model (GMM) image filtering and mean-squared error (MSE) optimization. The GMM-based image filtering method is achieved according to a probability threshold that removes the clusters whose likelihood are negligible in the non-specific regions. The MSE optimization method consists of a linear transformation that is obtained by minimizing the MSE in the non-specific region between the intensity normalized image and the template. The proposed intensity normalization methods are compared to: i) a standard approach based on the specific-to-non-specific binding ratio that is widely used, and ii) a linear approach based on the α-stable distribution. This comparison is performed on a DaTSCAN image database comprising analysis and classification stages for the development of a computer aided diagnosis (CAD) system for Parkinsonian syndrome (PS) detection. In addition, these proposed methods correct spatially varying artifacts that modulate the intensity of the images. Finally, using the leave-one-out cross-validation technique over these two approaches, the system achieves results up to a 92.91% of accuracy, 94.64% of sensitivity and 92.65 % of specificity, outperforming previous approaches based on a standard and a linear approach, which are used as a reference. The use of advanced intensity normalization techniques, such as the GMM-based image filtering and the MSE optimization improves the diagnosis of PS. | es_ES |
dc.description.sponsorship | This work was partly supported by the MICINN under the TEC2012-34306 project and the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain) under the Excellence Project P11-TIC-7103. This work has also been supported by a Marie Curie Intra-European Fellowship from the 7th Framework Programme FP7-PEOPLE-2013-IEF (Project: 624453 ALPHA-BRAIN-IMAGING) and ERASMUS MUNDUS AL-IDRISI I. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Public Library of Science (Plos) | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/FP7/624453 | es_ES |
dc.rights | Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/ | es_ES |
dc.subject | Imaging techniques | es_ES |
dc.subject | Neostriatum | es_ES |
dc.subject | Single photon emissions computed tomography | es_ES |
dc.subject | Neuroimaging | es_ES |
dc.subject | Optimization | es_ES |
dc.subject | Parkinson disease | es_ES |
dc.subject | Principal component analysis | es_ES |
dc.subject | Image analysis | es_ES |
dc.title | Comparison between Different Intensity Normalization Methods in 123I-Ioflupane Imaging for the Automatic Detection of Parkinsonism | es_ES |
dc.type | journal article | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.identifier.doi | 10.1371/journal.pone.0130274 | |