Application of regularized Richardson-Lucy algorithm for deconvolution of confocal microscopy images

Laasmaa, Martin; Vendelin, Marko; Peterson, Pearu

doi:10.1111/j.1365-2818.2011.03486.x

Cited by 85 publications

(56 citation statements)

References 40 publications

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“…In the future, real‐time imaging recovery can potentially be achieved by further optimizing the algorithm or advancing hardware such as graphic processing unit. BDTV approach is inherently noise‐tolerant because it is developed based on the RL deconvolution algorithm that is proved to be robust in the presence of noise . Our results of enhanced lateral resolution (Figure ) and imaging SNR (Figures , and ) confirm the robust performance of the BDTV operation for the PAOM chorioretinal imaging recovery.…”

Section: Resultssupporting

confidence: 65%

“…In addition, the algorithm lacks stability and uniqueness owing to the ill‐posed nature of inverse problem. TV, initially established as the regularization constraint for imaging restoration , demonstrates great success for the inverse problems based on its excellent performance on smoothing homogeneous regions while preserving object edge . In the case of the BD algorithm with the TV regularization (BDTV), let h ( k ) ( x , y ) and o ( k ) ( x , y ) denote the estimations obtained at the k ‐th iteration, which can be written as:

h_{t}^{((), k)} (x, y) = [\frac{g (x, y)}{h^{((), k - 1)} (x, y) * o^{((), k - 1)} (x, y)} * o^{((), k - 1)} (- x, - y)] h^{((), k - 1)} (x, y), h^{((), k)} (x, y) = \frac{h_{t}^{((), k)} (x, y)}{\sum_{x, y} h_{t}^{((), k)} (x, y)}

\begin{matrix} lefttrue \\ o^{((), k)} (x, y) = [\frac{g (x, y)}{o^{((), k - 1)} (x, y) * h^{((), k)} (x, y)} * h^{((), k)} (- x, - y)] o^{((), k - 1)} (x, y) \\ \times \frac{1}{1 - λdiv (\frac{\nabla o^{(k - 1)} ((),, x, y)}{|\nabla o^{(())}|})} \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

In vivo blind‐deconvolution photoacoustic ophthalmoscopy with total variation regularization

Xie

Gao

et al. 2018

Journal of Biophotonics

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Photoacoustic ophthalmoscopy (PAOM) is capable of noninvasively imaging anatomic and functional information of the retina in living rodents. However, the strong ocular aberration in rodent eyes and limited ultrasonic detection sensitivity affect PAOM's spatial resolution and signal-to-noise ratio (SNR) in in vivo eyes. In this work, we report a computational approach to combine blind deconvolution (BD) algorithm with a regularizing constraint based on total variation (BDTV) for PAOM imaging restoration. We tested the algorithm in retinal and choroidal microvascular images in albino rat eyes. The algorithm improved PAOM's lateral resolution by around 2-fold. Moreover, it enabled the improvement in imaging SNR for both major vessels and capillaries, and realized the well-preserved blood vessels' edges simultaneously, which surpasses conventional Richardson-Lucy BD algorithm. The reported results indicate that the BDTV algorithm potentially facilitate PAOM in extracting retinal pathophysiological information by enhancing in vivo imaging quality without physically modifying PAOM's optical configuration.

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Section: Resultssupporting

confidence: 65%

h_{t}^{((), k)} (x, y) = [\frac{g (x, y)}{h^{((), k - 1)} (x, y) * o^{((), k - 1)} (x, y)} * o^{((), k - 1)} (- x, - y)] h^{((), k - 1)} (x, y), h^{((), k)} (x, y) = \frac{h_{t}^{((), k)} (x, y)}{\sum_{x, y} h_{t}^{((), k)} (x, y)}

\begin{matrix} lefttrue \\ o^{((), k)} (x, y) = [\frac{g (x, y)}{o^{((), k - 1)} (x, y) * h^{((), k)} (x, y)} * h^{((), k)} (- x, - y)] o^{((), k - 1)} (x, y) \\ \times \frac{1}{1 - λdiv (\frac{\nabla o^{(k - 1)} ((),, x, y)}{|\nabla o^{(())}|})} \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

In vivo blind‐deconvolution photoacoustic ophthalmoscopy with total variation regularization

Xie

Gao

et al. 2018

Journal of Biophotonics

View full text Add to dashboard Cite

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“…Total variation prior. (Rudin et al, 1992;Dey et al, 2004Dey et al, , 2006Hovden et al, 2011;Laasmaa et al, 2011). The TV prior (Rudin et al, 1992) assumes that natural images x should consist of flat regions delineated by a relatively small amount of edges.…”

Section: Bayesian Image Restorationmentioning

confidence: 99%

An overview of state‐of‐the‐art image restoration in electron microscopy

Roels

Aelterman

Luong

et al. 2018

Journal of Microscopy

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In Life Science research, electron microscopy (EM) is an essential tool for morphological analysis at the subcellular level as it allows for visualization at nanometer resolution. However, electron micrographs contain image degradations such as noise and blur caused by electromagnetic interference, electron counting errors, magnetic lens imperfections, electron diffraction, etc. These imperfections in raw image quality are inevitable and hamper subsequent image analysis and visualization. In an effort to mitigate these artefacts, many electron microscopy image restoration algorithms have been proposed in the last years. Most of these methods rely on generic assumptions on the image or degradations and are therefore outperformed by advanced methods that are based on more accurate models. Ideally, a method will accurately model the specific degradations that fit the physical acquisition settings. In this overview paper, we discuss different electron microscopy image degradation solutions and demonstrate that dedicated artefact regularisation results in higher quality restoration and is applicable through recently developed probabilistic methods.

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“…Given the power of the python C-extension API, available libraries, and the ability for rapid and robust open software development, other microscopy software application have recently emerged, albeit with slightly different scientific goals, but based upon a similar python/C design philosophy. Two recent open source tools also written in python and C/C++, which have recently been reported in the literature for microscopy applications, are IOCBioMicroscope [20] (focused upon deconvolution of microscopy images) and BioImageXD [21]. …”

Section: Introductionmentioning

confidence: 99%

Software tool for 3D extraction of germinal centers

2013

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BackgroundGerminal Centers (GC) are short-lived micro-anatomical structures, within lymphoid organs, where affinity maturation is initiated. Theoretical modeling of the dynamics of the GC reaction including follicular CD4+ T helper and the recently described follicular regulatory CD4+ T cell populations, predicts that the intensity and life span of such reactions is driven by both types of T cells, yet controlled primarily by follicular regulatory CD4+ T cells. In order to calibrate GC models, it is necessary to properly analyze the kinetics of GC sizes. Presently, the estimation of spleen GC volumes relies upon confocal microscopy images from 20-30 slices spanning a depth of ~ 20 - 50 μm, whose GC areas are analyzed, slice-by-slice, for subsequent 3D reconstruction and quantification. The quantity of data to be analyzed from such images taken for kinetics experiments is usually prohibitively large to extract semi-manually with existing software. As a result, the entire procedure is highly time-consuming, and inaccurate, thereby motivating the need for a new software tool that can automatically identify and calculate the 3D spot volumes from GC multidimensional images.ResultsWe have developed pyBioImage, an open source cross platform image analysis software application, written in python with C extensions that is specifically tailored to the needs of immunologic research involving 4D imaging of GCs. The software provides 1) support for importing many multi-image formats, 2) basic image processing and analysis, and 3) the ExtractGC module, that allows for automatic analysis and visualization of extracted GC volumes from multidimensional confocal microscopy images. We present concrete examples of different microscopy image data sets of GC that have been used in experimental and theoretical studies of mouse model GC dynamics.ConclusionsThe pyBioImage software framework seeks to be a general purpose image application for immunological research based on 4D imaging. The ExtractGC module uses a novel clustering algorithm for automatically extracting quantitative spatial information of a large number of GCs from a collection of confocal microscopy images. In addition, the software provides 3D visualization of the GCs reconstructed from the image stacks. The application is available for public use at http://sourceforge.net/projects/pybioimage/.

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Application of regularized Richardson-Lucy algorithm for deconvolution of confocal microscopy images

Cited by 85 publications

References 40 publications

In vivo blind‐deconvolution photoacoustic ophthalmoscopy with total variation regularization

In vivo blind‐deconvolution photoacoustic ophthalmoscopy with total variation regularization

An overview of state‐of‐the‐art image restoration in electron microscopy

Software tool for 3D extraction of germinal centers

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