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Model Convolution: A Computational Approach to Digital Image Interpretation.

Gardner MK, Sprague BL, Pearson CG, Cosgrove BD, Bicek AD, Bloom K, Salmon ED, Odde DJ - Cell Mol Bioeng (2010)

Bottom Line: We then compare model-convolution to the more standard approach of experimental deconvolution.In some circumstances, standard experimental deconvolution approaches fail to yield the correct underlying fluorophore distribution.In these situations, model-convolution removes the uncertainty associated with deconvolution and therefore allows direct statistical comparison of experimental and theoretical data.

View Article: PubMed Central - PubMed

ABSTRACT
Digital fluorescence microscopy is commonly used to track individual proteins and their dynamics in living cells. However, extracting molecule-specific information from fluorescence images is often limited by the noise and blur intrinsic to the cell and the imaging system. Here we discuss a method called "model-convolution," which uses experimentally measured noise and blur to simulate the process of imaging fluorescent proteins whose spatial distribution cannot be resolved. We then compare model-convolution to the more standard approach of experimental deconvolution. In some circumstances, standard experimental deconvolution approaches fail to yield the correct underlying fluorophore distribution. In these situations, model-convolution removes the uncertainty associated with deconvolution and therefore allows direct statistical comparison of experimental and theoretical data. Thus, if there are structural constraints on molecular organization, the model-convolution method better utilizes information gathered via fluorescence microscopy, and naturally integrates experiment and theory.

No MeSH data available.


High noise levels limit the utility of the image deconvolution method. (a1) A simulated point-source fluorophore has been convolved with a theoretical PSF (no background noise) to produce a 32 × 32 image having a single signal in the center of the field. (a2) Subsequent image deconvolution (by Wiener filtering-based deconvolution using the Matlab image processing toolbox) precisely resolves the spreading of light due to the PSF, and correctly identifies the fluorophore location to be at the center. (b1) A simulated point-source fluorophore has been convolved with a theoretical PSF, but noise has been added to the image such that the SNR = 8. (b2) In this case, subsequent image deconvolution is able to correctly resolve the fluorophore location. (b3) In another image with SNR = 8, image deconvolution is not able to separate the fluorophore from background noise and misidentifies the location of the point source. (c) The ability of Wiener-filter-based deconvolution to separate fluorophores from background noise decreases substantially with decreasing SNR. The quantitative relationship between the failure rate and the SNR depends upon the specifics of the problem, but generally failure rate increases with decreasing SNR
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Fig2: High noise levels limit the utility of the image deconvolution method. (a1) A simulated point-source fluorophore has been convolved with a theoretical PSF (no background noise) to produce a 32 × 32 image having a single signal in the center of the field. (a2) Subsequent image deconvolution (by Wiener filtering-based deconvolution using the Matlab image processing toolbox) precisely resolves the spreading of light due to the PSF, and correctly identifies the fluorophore location to be at the center. (b1) A simulated point-source fluorophore has been convolved with a theoretical PSF, but noise has been added to the image such that the SNR = 8. (b2) In this case, subsequent image deconvolution is able to correctly resolve the fluorophore location. (b3) In another image with SNR = 8, image deconvolution is not able to separate the fluorophore from background noise and misidentifies the location of the point source. (c) The ability of Wiener-filter-based deconvolution to separate fluorophores from background noise decreases substantially with decreasing SNR. The quantitative relationship between the failure rate and the SNR depends upon the specifics of the problem, but generally failure rate increases with decreasing SNR

Mentions: To illustrate the effect of background noise on the accuracy of experimental deconvolution, we convolved a single fluorophore with a theoretical PSF, and then deconvolved the resulting image using a standard Wiener filter-based deconvolution algorithm, using the identical theoretical PSF (Fig. 2a). By adjusting background noise, we were able to achieve varying levels of success in identifying the simulated fluorophore after image deconvolution. Here, we define “success” as a result in which the brightest pixel in the deconvolved image is in the location of the original simulated fluorophore.Figure 2


Model Convolution: A Computational Approach to Digital Image Interpretation.

Gardner MK, Sprague BL, Pearson CG, Cosgrove BD, Bicek AD, Bloom K, Salmon ED, Odde DJ - Cell Mol Bioeng (2010)

High noise levels limit the utility of the image deconvolution method. (a1) A simulated point-source fluorophore has been convolved with a theoretical PSF (no background noise) to produce a 32 × 32 image having a single signal in the center of the field. (a2) Subsequent image deconvolution (by Wiener filtering-based deconvolution using the Matlab image processing toolbox) precisely resolves the spreading of light due to the PSF, and correctly identifies the fluorophore location to be at the center. (b1) A simulated point-source fluorophore has been convolved with a theoretical PSF, but noise has been added to the image such that the SNR = 8. (b2) In this case, subsequent image deconvolution is able to correctly resolve the fluorophore location. (b3) In another image with SNR = 8, image deconvolution is not able to separate the fluorophore from background noise and misidentifies the location of the point source. (c) The ability of Wiener-filter-based deconvolution to separate fluorophores from background noise decreases substantially with decreasing SNR. The quantitative relationship between the failure rate and the SNR depends upon the specifics of the problem, but generally failure rate increases with decreasing SNR
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2864900&req=5

Fig2: High noise levels limit the utility of the image deconvolution method. (a1) A simulated point-source fluorophore has been convolved with a theoretical PSF (no background noise) to produce a 32 × 32 image having a single signal in the center of the field. (a2) Subsequent image deconvolution (by Wiener filtering-based deconvolution using the Matlab image processing toolbox) precisely resolves the spreading of light due to the PSF, and correctly identifies the fluorophore location to be at the center. (b1) A simulated point-source fluorophore has been convolved with a theoretical PSF, but noise has been added to the image such that the SNR = 8. (b2) In this case, subsequent image deconvolution is able to correctly resolve the fluorophore location. (b3) In another image with SNR = 8, image deconvolution is not able to separate the fluorophore from background noise and misidentifies the location of the point source. (c) The ability of Wiener-filter-based deconvolution to separate fluorophores from background noise decreases substantially with decreasing SNR. The quantitative relationship between the failure rate and the SNR depends upon the specifics of the problem, but generally failure rate increases with decreasing SNR
Mentions: To illustrate the effect of background noise on the accuracy of experimental deconvolution, we convolved a single fluorophore with a theoretical PSF, and then deconvolved the resulting image using a standard Wiener filter-based deconvolution algorithm, using the identical theoretical PSF (Fig. 2a). By adjusting background noise, we were able to achieve varying levels of success in identifying the simulated fluorophore after image deconvolution. Here, we define “success” as a result in which the brightest pixel in the deconvolved image is in the location of the original simulated fluorophore.Figure 2

Bottom Line: We then compare model-convolution to the more standard approach of experimental deconvolution.In some circumstances, standard experimental deconvolution approaches fail to yield the correct underlying fluorophore distribution.In these situations, model-convolution removes the uncertainty associated with deconvolution and therefore allows direct statistical comparison of experimental and theoretical data.

View Article: PubMed Central - PubMed

ABSTRACT
Digital fluorescence microscopy is commonly used to track individual proteins and their dynamics in living cells. However, extracting molecule-specific information from fluorescence images is often limited by the noise and blur intrinsic to the cell and the imaging system. Here we discuss a method called "model-convolution," which uses experimentally measured noise and blur to simulate the process of imaging fluorescent proteins whose spatial distribution cannot be resolved. We then compare model-convolution to the more standard approach of experimental deconvolution. In some circumstances, standard experimental deconvolution approaches fail to yield the correct underlying fluorophore distribution. In these situations, model-convolution removes the uncertainty associated with deconvolution and therefore allows direct statistical comparison of experimental and theoretical data. Thus, if there are structural constraints on molecular organization, the model-convolution method better utilizes information gathered via fluorescence microscopy, and naturally integrates experiment and theory.

No MeSH data available.