Sparsity based denoising of spectral domain optical coherence tomography images

Leyuan Fang*, Shutao Li, Qing Nie, Joseph A. Izatt, Cynthia A. Toth, Sina Farsiu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

235 Citations (Scopus)

Abstract

In this paper, we make contact with the field of compressive sensing and present a development and generalization of tools and results for reconstructing irregularly sampled tomographic data. In particular, we focus on denoising Spectral-Domain Optical Coherence Tomography (SDOCT) volumetric data. We take advantage of customized scanning patterns, in which, a selected number of B-scans are imaged at higher signal-to-noise ratio (SNR). We learn a sparse representation dictionary for each of these high-SNR images, and utilize such dictionaries to denoise the low-SNR B-scans. We name this method multiscale sparsity based tomographic denoising (MSBTD). We show the qualitative and quantitative superiority of the MSBTD algorithm compared to popular denoising algorithms on images from normal and age-related macular degeneration eyes of a multi-center clinical trial. We have made the corresponding data set and software freely available online.

Original languageEnglish
Pages (from-to)927-942
Number of pages16
JournalBiomedical Optics Express
Volume3
Issue number5
DOIs
Publication statusPublished - 1 May 2012

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