Sparsity based denoising of spectral domain optical coherence tomography images

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

doi:10.1364/BOE.3.000927

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

School of Information and Electronics

Research output: Contribution to journal › Article › peer-review

238 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 language	English
Pages (from-to)	927-942
Number of pages	16
Journal	Biomedical Optics Express
Volume	3
Issue number	5
DOIs	https://doi.org/10.1364/BOE.3.000927
Publication status	Published - 1 May 2012

Access to Document

10.1364/BOE.3.000927

Cite this

@article{78e75b29b8cb40808b144d8f81b20d4c,

title = "Sparsity based denoising of spectral domain optical coherence tomography images",

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.",

author = "Leyuan Fang and Shutao Li and Qing Nie and Izatt, {Joseph A.} and Toth, {Cynthia A.} and Sina Farsiu",

year = "2012",

month = may,

day = "1",

doi = "10.1364/BOE.3.000927",

language = "English",

volume = "3",

pages = "927--942",

journal = "Biomedical Optics Express",

issn = "2156-7085",

publisher = "Optica Publishing Group (formerly OSA)",

number = "5",

}

TY - JOUR

T1 - Sparsity based denoising of spectral domain optical coherence tomography images

AU - Fang, Leyuan

AU - Li, Shutao

AU - Nie, Qing

AU - Izatt, Joseph A.

AU - Toth, Cynthia A.

AU - Farsiu, Sina

PY - 2012/5/1

Y1 - 2012/5/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84863946656&partnerID=8YFLogxK

U2 - 10.1364/BOE.3.000927

DO - 10.1364/BOE.3.000927

M3 - Article

AN - SCOPUS:84863946656

SN - 2156-7085

VL - 3

SP - 927

EP - 942

JO - Biomedical Optics Express

JF - Biomedical Optics Express

IS - 5

ER -

Sparsity based denoising of spectral domain optical coherence tomography images

Abstract

Access to Document

Other files and links

Fingerprint

Cite this