Research Papers: Imaging

Enhanced low-rank + sparsity decomposition for speckle reduction in optical coherence tomography

[+] Author Affiliations
Ivica Kopriva

Ruđer Bošković Institute, Division of Electronics, Bijenička cesta 54, Zagreb 10002, Croatia

Fei Shi, Xinjian Chen

Soochow University, School of Electronics and Information Engineering, No. 1 Shizi Street, Suzhou City 215006, China

J. Biomed. Opt. 21(7), 076008 (Jul 18, 2016). doi:10.1117/1.JBO.21.7.076008
History: Received April 28, 2016; Accepted June 28, 2016
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Abstract.  Speckle artifacts can strongly hamper quantitative analysis of optical coherence tomography (OCT), which is necessary to provide assessment of ocular disorders associated with vision loss. Here, we introduce a method for speckle reduction, which leverages from low-rank + sparsity decomposition (LRpSD) of the logarithm of intensity OCT images. In particular, we combine nonconvex regularization-based low-rank approximation of an original OCT image with a sparsity term that incorporates the speckle. State-of-the-art methods for LRpSD require a priori knowledge of a rank and approximate it with nuclear norm, which is not an accurate rank indicator. As opposed to that, the proposed method provides more accurate approximation of a rank through the use of nonconvex regularization that induces sparse approximation of singular values. Furthermore, a rank value is not required to be known a priori. This, in turn, yields an automatic and computationally more efficient method for speckle reduction, which yields the OCT image with improved contrast-to-noise ratio, contrast and edge fidelity. The source code will be available at www.mipav.net/English/research/research.html.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Ivica Kopriva ; Fei Shi and Xinjian Chen
"Enhanced low-rank + sparsity decomposition for speckle reduction in optical coherence tomography", J. Biomed. Opt. 21(7), 076008 (Jul 18, 2016). ; http://dx.doi.org/10.1117/1.JBO.21.7.076008


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