Parallel techniques for speckle noise reduction in medical ultrasound images

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/109509
Información del item - Informació de l'item - Item information
Title: Parallel techniques for speckle noise reduction in medical ultrasound images
Authors: Arnal, Josep | Mayzel, Ilya
Research Group/s: Computación de Altas Prestaciones y Paralelismo (gCAPyP)
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Parallel computing | Medical ultrasound imaging | Noise reduction | Speckle noise | Speckle reduction
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: Oct-2020
Publisher: Elsevier
Citation: Advances in Engineering Software. 2020, 148: 102867. https://doi.org/10.1016/j.advengsoft.2020.102867
Abstract: Parallel computing is used to accelerate the computations in the reduction of speckle noise in medical ultrasound images. A new hybrid algorithm for speckle noise reduction in medical ultrasound images is proposed and compared to an existing efficient method. The method combines in an efficient manner the advantages of several denoising filters using local and non-local information. A comparative study is carried out using quantitative and qualitative measures and showing the competitiveness of the proposed method. Both methods are parallelized using OpenMP, and a hybrid combination of MPI and OpenMP. The parallel implementations are compared to the sequential ones, obtaining significative values of speedup.
Sponsor: This research was supported by the Spanish Ministry of Science, Innovation and Universities (Grant RTI2018-098156-B-C54) co-financed by FEDER funds.
URI: http://hdl.handle.net/10045/109509
ISSN: 0965-9978 (Print) | 1873-5339 (Online)
DOI: 10.1016/j.advengsoft.2020.102867
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2020 Elsevier Ltd.
Peer Review: si
Publisher version: https://doi.org/10.1016/j.advengsoft.2020.102867
Appears in Collections:INV - gCAPyP - Artículos de Revistas

Files in This Item:
Files in This Item:
File Description SizeFormat 
ThumbnailArnal_Mayzel_2020_AdvEngSoft_final.pdfVersión final (acceso restringido)11,59 MBAdobe PDFOpen    Request a copy


Items in RUA are protected by copyright, with all rights reserved, unless otherwise indicated.