Statistical model for OCT image denoising

Muxingzi Li, Ramzi Idoughi, Biswarup Choudhury, Wolfgang Heidrich

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

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.
Original languageEnglish (US)
Pages (from-to)3903
JournalBiomedical Optics Express
Volume8
Issue number9
DOIs
StatePublished - Aug 1 2017

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