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MRI noise estimation and denoising using non-local PCA

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MRI noise estimation and denoising using non-local PCA

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dc.contributor.author Manjón Herrera, José Vicente es_ES
dc.contributor.author Coupé, Pierrick es_ES
dc.contributor.author Buades, Antonio es_ES
dc.date.accessioned 2016-05-23T14:24:12Z
dc.date.available 2016-05-23T14:24:12Z
dc.date.issued 2015-05
dc.identifier.issn 1361-8415
dc.identifier.uri http://hdl.handle.net/10251/64623
dc.description NOTICE: this is the author’s version of a work that was accepted for publication in Medical Image AnalysisChanges resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Medical Image Analysis, [Volume 22, Issue 1, May 2015, Pages 35–47] DOI 10.1016/j.media.2015.01.004 es_ES
dc.description.abstract This paper proposes a novel method for MRI denoising that exploits both the sparseness and self-similarity properties of the MR images. The proposed method is a two-stage approach that first filters the noisy image using a non local PCA thresholding strategy by automatically estimating the local noise level present in the image and second uses this filtered image as a guide image within a rotationally invariant non-local means filter. The proposed method internally estimates the amount of local noise presents in the images that enables applying it automatically to images with spatially varying noise levels and also corrects the Rician noise induced bias locally. The proposed approach has been compared with related state-of-the-art methods showing competitive results in all the studied cases. es_ES
dc.description.sponsorship We are grateful to Dr. Matteo Mangioni and Dr. Alessandro Foi for their help on running their BM4D method in our comparisons. We want also to thank Dr. Luis Marti-Bonmati and Dr. Angel Alberich-Bayarri from Quiron Hospital of Valencia for providing the real clinical data used in this paper. This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Programme IdEx Bordeaux (ANR-10-IDEX-03-02), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57). en_EN
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation French National Research Agency (ANR) in the frame of the Investments for the future Programme IdEx Bordeaux (ANR-10-IDEX-03-02), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57) es_ES
dc.relation.ispartof Medical Image Analysis es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject MRI es_ES
dc.subject Denosing es_ES
dc.subject PCA es_ES
dc.subject Sparseness es_ES
dc.subject Non-local means es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title MRI noise estimation and denoising using non-local PCA es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.media.2015.01.004
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada es_ES
dc.description.bibliographicCitation Manjón Herrera, JV.; Coupé, P.; Buades, A. (2015). MRI noise estimation and denoising using non-local PCA. Medical Image Analysis. 22(1):35-47. doi:10.1016/j.media.2015.01.004 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.media.2015.01.004 es_ES
dc.description.upvformatpinicio 35 es_ES
dc.description.upvformatpfin 47 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 22 es_ES
dc.description.issue 1 es_ES
dc.relation.senia 285687 es_ES


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