Denoising optical coherence tomography using second order total generalized variation decomposition

Duan, Jinming; Lu, Wenqi; Tench, Christopher R.; Gottlob, Irène; Proudlock, Frank A; Samani, Niraj N; Li, Bai

doi:10.1016/j.bspc.2015.09.012

Cited by 88 publications

(52 citation statements)

References 29 publications

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“…To evaluate the merits of the proposed method, we compare against 5 state-of-the-art speckle reduction methods: multiscale sparsity based tomographic denoising (MSBTD) with the logspace transformation [11], log-space BM3D [29], complex wavelet based K-SVD [24], general Bayesian estimation method [18], and TGV decomposition [16]. All processing is implemented…”

Section: Resultsmentioning

confidence: 99%

“…Image-domain methods often adopt regularizers from the field of image processing, e.g. TV regularization assuming a Gamma distribution for the speckle [14], secondorder total generalized variation (TGV) based on the classical Vese-Osher (VO) decomposition model [15,16], and PDE-Based nonlinear diffusion [17]. Probabilistic approaches have also been developed for denoising OCT images.…”

Section: Introductionmentioning

confidence: 99%

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Statistical model for OCT image denoising

Idoughi

Choudhury

et al. 2017

Biomed. Opt. Express

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical model for OCT image denoising

Idoughi

Choudhury

et al. 2017

Biomed. Opt. Express

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“…The denoising of the original image was implemented by the TGV method [43], which is a powerful image pre-processing tool that has been extensively used in image processing community [44][45][46]. The TGV regularisation has the capability of representing image characteristics up to an arbitrary order of differentiation (piecewise constant, piecewise affine, piecewise quadratic etc.).…”

Section: Image Analysis Algorithmmentioning

confidence: 99%

<strong>A portable image-based cytometer for rapid malaria detection and quantification</strong>

Yang

Subramanian

Duan

et al. 2017

Proceedings of the 7th International Multidisciplinary Conference on Optofluidics 2017

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Increasing resistance by malaria parasites to currently used antimalarials across the developing world warrants timely detection and classification so that appropriate drug combinations can be administered before clinical complications arise. However, this is often challenged by low levels of infection (referred to as parasitemia) and presence of predominantly young parasitic forms in the patients' peripheral blood. Herein, we developed a simple, inexpensive and portable image-based cytometer that detects and numerically counts Plasmodium falciparum infected red blood cells (iRBCs) from Giemsa-stained smears derived from infected blood. Our cytometer is able to classify all parasitic subpopulations by quantifying the area occupied by the parasites within iRBCs, with high specificity, sensitivity and negligible false positives (~0.0025%). Moreover, we demonstrate the application of our image-based cytometer in testing anti-malarial efficacy against a commercial flow cytometer and demonstrate comparable results between the two methods. Collectively, these results highlight the possibility to use our image-based cytometer as a cheap, rapid and accurate alternative for antimalarial testing without compromising on efficiency and minimal processing time. With appropriate filters applied into the algorithm, to rule out leukocytes and reticulocytes, our cytometer may also be used for field diagnosis of malaria.

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“…Unlike the high order variational models, such as the Gaussian curvature [28], mean curvature [23,29], Euler's elastica [21] etc., the SOTV is a convex high order extension of the FOTV, which guarantees a global solution. The SOTV is also more efficient to implement [4] than the convex total generalised variation (TGV) [30,31]. However, the inpainting results of the model highly depend on the geometry of the inpainting region, and it also tends to blur the inpainted area [2,22].…”

Section: Introductionmentioning

confidence: 99%

Introducing diffusion tensor to high order variational model for image reconstruction

Duan

Ward

Sibbett

et al. 2017

Digital Signal Processing

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The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk JID:YDSPR AID:2153 /FLA [m5G; v1.219; Prn:17/07/2017; 11:02] Second order total variation (SOTV) models have advantages for image restoration over their first order counterparts including their ability to remove the staircase artefact in the restored image. However, such models tend to blur the reconstructed image when discretised for numerical solution [1][2][3][4][5]. To overcome this drawback, we introduce a new tensor weighted second order (TWSO) model for image restoration. Specifically, we develop a novel regulariser for the SOTV model that uses the Frobenius norm of the product of the isotropic SOTV Hessian matrix and an anisotropic tensor. We then adapt the alternating direction method of multipliers (ADMM) to solve the proposed model by breaking down the original problem into several subproblems. All the subproblems have closed-forms and can be solved efficiently. The proposed method is compared with state-of-the-art approaches such as tensor-based anisotropic diffusion, total generalised variation, and Euler's elastica. We validate the proposed TWSO model using extensive experimental results on a large number of images from the Berkeley BSDS500. We also demonstrate that our method effectively reduces both the staircase and blurring effects and outperforms existing approaches for image inpainting and denoising applications.

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Denoising optical coherence tomography using second order total generalized variation decomposition

Cited by 88 publications

References 29 publications

Statistical model for OCT image denoising

Statistical model for OCT image denoising

<strong>A portable image-based cytometer for rapid malaria detection and quantification</strong>

Introducing diffusion tensor to high order variational model for image reconstruction

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