Statistical model for OCT image denoising

Handle URI:
http://hdl.handle.net/10754/625719
Title:
Statistical model for OCT image denoising
Authors:
Li, Muxingzi ( 0000-0003-2126-8578 ) ; Idoughi, Ramzi; Choudhury, Biswarup; Heidrich, Wolfgang ( 0000-0002-4227-8508 )
Abstract:
Optical coherence tomography (OCT) is a non-invasive technique with a large array of applications in clinical imaging and biological tissue visualization. However, the presence of speckle noise affects the analysis of OCT images and their diagnostic utility. In this article, we introduce a new OCT denoising algorithm. The proposed method is founded on a numerical optimization framework based on maximum-a-posteriori estimate of the noise-free OCT image. It combines a novel speckle noise model, derived from local statistics of empirical spectral domain OCT (SD-OCT) data, with a Huber variant of total variation regularization for edge preservation. The proposed approach exhibits satisfying results in terms of speckle noise reduction as well as edge preservation, at reduced computational cost.
KAUST Department:
King Abdullah University of science and Technology, Thuwal 23955-6900, Saudi Arabia
Citation:
Li M, Idoughi R, Choudhury B, Heidrich W (2017) Statistical model for OCT image denoising. Biomedical Optics Express 8: 3903. Available: http://dx.doi.org/10.1364/boe.8.003903.
Publisher:
The Optical Society
Journal:
Biomedical Optics Express
Issue Date:
1-Aug-2017
DOI:
10.1364/boe.8.003903
Type:
Article
ISSN:
2156-7085; 2156-7085
Sponsors:
King Abdullah University of Science and Technology (KAUST) (Visual Computing Center Competitive Funding). We would like to thank Prof. TorOve Leiknes and Luca Fortunato of Water Desalination and Reuse Center, KAUST for help with the OCT data acquisition. We also take this opportunity to thank the ThorLabs, Munich engineering team for their assistance with the native OCT file handling procedures. The authors would also like to thank Mohamed Aly for his valuable feedback on the project.
Additional Links:
https://www.osapublishing.org/boe/abstract.cfm?uri=boe-8-9-3903
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorLi, Muxingzien
dc.contributor.authorIdoughi, Ramzien
dc.contributor.authorChoudhury, Biswarupen
dc.contributor.authorHeidrich, Wolfgangen
dc.date.accessioned2017-10-03T12:49:35Z-
dc.date.available2017-10-03T12:49:35Z-
dc.date.issued2017-08-01en
dc.identifier.citationLi M, Idoughi R, Choudhury B, Heidrich W (2017) Statistical model for OCT image denoising. Biomedical Optics Express 8: 3903. Available: http://dx.doi.org/10.1364/boe.8.003903.en
dc.identifier.issn2156-7085en
dc.identifier.issn2156-7085en
dc.identifier.doi10.1364/boe.8.003903en
dc.identifier.urihttp://hdl.handle.net/10754/625719-
dc.description.abstractOptical coherence tomography (OCT) is a non-invasive technique with a large array of applications in clinical imaging and biological tissue visualization. However, the presence of speckle noise affects the analysis of OCT images and their diagnostic utility. In this article, we introduce a new OCT denoising algorithm. The proposed method is founded on a numerical optimization framework based on maximum-a-posteriori estimate of the noise-free OCT image. It combines a novel speckle noise model, derived from local statistics of empirical spectral domain OCT (SD-OCT) data, with a Huber variant of total variation regularization for edge preservation. The proposed approach exhibits satisfying results in terms of speckle noise reduction as well as edge preservation, at reduced computational cost.en
dc.description.sponsorshipKing Abdullah University of Science and Technology (KAUST) (Visual Computing Center Competitive Funding). We would like to thank Prof. TorOve Leiknes and Luca Fortunato of Water Desalination and Reuse Center, KAUST for help with the OCT data acquisition. We also take this opportunity to thank the ThorLabs, Munich engineering team for their assistance with the native OCT file handling procedures. The authors would also like to thank Mohamed Aly for his valuable feedback on the project.en
dc.publisherThe Optical Societyen
dc.relation.urlhttps://www.osapublishing.org/boe/abstract.cfm?uri=boe-8-9-3903en
dc.subjectImage enhancementen
dc.subjectNoise in imaging systemsen
dc.subjectOptical coherence tomographyen
dc.subjectSpeckleen
dc.subjectStatistical opticsen
dc.titleStatistical model for OCT image denoisingen
dc.typeArticleen
dc.contributor.departmentKing Abdullah University of science and Technology, Thuwal 23955-6900, Saudi Arabiaen
dc.identifier.journalBiomedical Optics Expressen
kaust.authorLi, Muxingzien
kaust.authorIdoughi, Ramzien
kaust.authorChoudhury, Biswarupen
kaust.authorHeidrich, Wolfgangen
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