Movatterモバイル変換

[0]ホーム
URL:

Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
Thehttps:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

NIH NLM Logo
Log inShow account info
Access keysNCBI HomepageMyNCBI HomepageMain ContentMain Navigation
pubmed logo
Advanced Clipboard
User Guide

Full text links

Elsevier Science full text link Elsevier Science Free PMC article
Full text links

Actions

Share

.2012 May;16(4):849-64.
doi: 10.1016/j.media.2012.01.002. Epub 2012 Jan 18.

A CANDLE for a deeper in vivo insight

Affiliations

A CANDLE for a deeper in vivo insight

Pierrick Coupé et al. Med Image Anal.2012 May.

Abstract

A new Collaborative Approach for eNhanced Denoising under Low-light Excitation (CANDLE) is introduced for the processing of 3D laser scanning multiphoton microscopy images. CANDLE is designed to be robust for low signal-to-noise ratio (SNR) conditions typically encountered when imaging deep in scattering biological specimens. Based on an optimized non-local means filter involving the comparison of filtered patches, CANDLE locally adapts the amount of smoothing in order to deal with the noise inhomogeneity inherent to laser scanning fluorescence microscopy images. An extensive validation on synthetic data, images acquired on microspheres and in vivo images is presented. These experiments show that the CANDLE filter obtained competitive results compared to a state-of-the-art method and a locally adaptive optimized non-local means filter, especially under low SNR conditions (PSNR<8dB). Finally, the deeper imaging capabilities enabled by the proposed filter are demonstrated on deep tissue in vivo images of neurons and fine axonal processes in the Xenopus tadpole brain.

Copyright © 2012 Elsevier B.V. All rights reserved.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Overall workflow of the proposed method.
Figure 2
Figure 2
CANDLE illuminated by an example. To obtain the denoised valued̂i at locationxt, the weightsw̃i,j computed on prefiltered image are used to aggregate the stabilized noisy samples Ta(zj)by taking into account the local noise level σ̂i.
Figure 3
Figure 3
Quantitative comparison of the CANDLE, OVST-LAONLM and PureDenoise filters on synthetic images corrupted by Poisson-Gaussian noise.Left: PSNR in dB of the denoised image according to PSNR of the noisy image.Right: Correlation between noise-free and the denoised image according to PSNR of the noisy image.
Figure 4
Figure 4
Denoising results obtained by the CANDLE, OVST-LAONLM and PureDenoise filters in experiments on synthetic images.Top: the noise-free synthetic image used during the experiment.Middle: noisy image at 16.5dB and the denoised images obtained by the methods compared.Bottom: noisy image at 6 dB (highest level of noise) and the denoised images obtained by the methods compared.
Figure 5
Figure 5
Left: the reference image obtained by averaging 40 acquisitions of the microsphere(15 μm diameter) at 3mW.Right: The intensity profile along the red line in the image on the left.
Figure 6
Figure 6
Quantitative comparison of the CANDLE, OVST-LAONLM and PureDenoise filters on images of the microsphere. Correlation between the reference image at 3mW and the denoised image according to excitation power in mW.
Figure 7
Figure 7
Denoising results obtained by the CANDLE, OVST-LAONLM and PureDenoise filters on images of a microshpere (15 μm diameter)..Top: noisy image at high power (2.5mW) and the denoised images obtained by the methods compared. The values of the red lines are plot for each filter and the corresponding correlation coefficients are given.Bottom: Similar results obtained at very low power (0.3mW). For denoised images, contrast was adjusted manually to minimize the appearance of residual spike noise to avoid saturation in the figure. For intensity profiles, identical axis range is used for all the methods and the noisy image. Saturation of the highest noise peaks was performed to display the signal of interest at an adapted scale.
Figure 8
Figure 8
The image stack of xenopus laevis tectal neurons used for thein-vivo experiment.Left: Maximum intensity projection along the z-axis through 19 slices of the reference stack obtained by averaging 20 registered acquisitions at 9mW. Middle: the central slice of the reference stack.Right: The intensity profile of the red line in the reference image.
Figure 9
Figure 9
Quantitative comparison of the CANDLE, OVST-LAONLM and PureDenoise filters onin vivo images ofxenopus laevis tectal neurons. Correlation between the reference image at 9mW build on 20 acquisitions and the individual denoised images according to acquisition power from 3mW to 9mW. Two different strategies to construct the reference image are compared. Left: The 20 acquisitions are classically averaged; the reference image is the mean of the 20 images. Right: The median of the 20 acquisitions is used as reference image.
Figure 10
Figure 10
Denoising results obtained by the CANDLE, OVST-LAONLM and PureDenoise filters applied to thein vivo experiments onXenopus laevis tectal neurons.Top (from left to right): noisy single image at highest power (9mW) and the corresponding denoised images obtained by the three methods compared. The correlation coefficient between each image and the mean reference image is provided just below. Second row: Intensity profiles for the horizontal line for the corresponding image in the first row. Third & fourth row: similar results obtained for images acquired at the lowest power (3mW). For denoised images, contrast was adjusted manually to minimize the appearance of residual spike noise to avoid saturation in the figure. For intensity profiles, identical axis range is used for all the methods and the noisy image. Saturation of the highest noise peaks was performed to display the signal of interest at an adapted scale.
Figure 11
Figure 11
Influence of filter parameters on denoising quality in term of PSNR and coefficient of correlation. The impact of β on denoising quality is studied from 0.1 up to 4.1 with different settings for patch radius (rp) and search volume radius (rsv). The test image used is thein vivo acquisition at 9mW and the reference image is the average image of 20 acquisitions at 9mW.
Figure 12
Figure 12
Comparison between collaborative approach used in CANDLE and traditional sequential approach. In the latter, the median filtered image is used as input in the OVST-LAONLM filter. Visually, the contrast of the sequential approach is lower than the result produced by CANDLE. Moreover, the finest dendritic structures are removed during denoising based on sequential approach as assessed by coefficients of correlation (CC). The MIPs of denoised images are displayed with the same intensity range.
Figure 13
Figure 13
Deep acquisition in aXenopus laevis brain with two channels: The denoised images were obtained in less than 160 seconds per channel (95 slices each) using the default parameter of CANDLE software for both channels in a fully automatic manner. The display intensity has been set independently for each scanning depth using ImageJ. For a given depth, the lookup tables for the noisy and the denoised images are identical.
Figure 14
Figure 14
Maximum intensity projection of channel 2 of the deep acquisition image of tadpole brain. The projection was achieved in the optic chiasm area between 275 μm – 320 μm. The lookup tables for displaying the noisy and the denoised images are identical.Top: the full images before and after denoising with red rectangles indicating the area enlarged below.Bottom: Zoomed images of the optic chiasm at the base of the brain before and after denoising.
Figure 15
Figure 15
Maximum intensity projection of olfactory neuron of frog. Left: Original acquired image. Right: Denoised image with CANDLE. Top: the full images before and after denoising. Bottom: Zoom on the central part of the image.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Anscombe FJ. The Transformation of Poisson, Binomial and Negative-Binomial Data. Biometrika. 1948;35:246–254.
    1. Arce GR. Nonlinear signal processing: a statistical approach. Wiley; Hoboken; Chichester: 2005.
    1. Baker M. Seeing more with less. Nat Methods. 2010;7:782. - PubMed
    1. Boulanger J, Kervrann C, Bouthemy P, Elbau P, Sibarita JB, Salamero J. Patch-based nonlocal functional for denoising fluorescence microscopy image sequences. IEEE Trans Med Imaging. 2010;29:442–454. - PubMed
    1. Broser PJ, Schulte R, Roth A, Helmehen F, Waters J, Lang S, Sakmann B, Wittum G. Nonlinear anisotropic diffusion filtering of three-dimensional image data from 2-photon microscopy. Image Processing: Algorithms and Systems IV. 2005;5672:44–69. - PubMed

Publication types

MeSH terms

Related information

Grants and funding

LinkOut - more resources

Full text links
Elsevier Science full text link Elsevier Science Free PMC article
Cite
Send To

NCBI Literature Resources

MeSHPMCBookshelfDisclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

[8]ページ先頭
©2009-2025 Movatter.jp