Journal: Cellular and Molecular Bioengineering

Article Title: Model Convolution: A Computational Approach to Digital Image Interpretation

doi: 10.1007/s12195-010-0101-7

Figure Lengend Snippet: Image deconvolution cannot always resolve individual fluorophore locations. (a) A typical yeast mitotic spindle experimental fluorescence image (kinetochore-associated fluorescence, green; spindle pole body fluorescence, red). (b) Theoretical point-source fluorophores (32 green points, representing individual kinetochores, and 2 red points, representing the spindle pole bodies) along a 1500 nm length. The bright green pixels indicate the presence of multiple fluorophores within the pixel area. For simplicity, it was assumed that there are no fluorophores in out-of-focus focal planes. (c) Point-source fluorophores in (a) are convolved with the microscope PSF and noise is added. (d) The image in (b) has been deconvolved using the identical PSF. The image deconvolution process cannot resolve the individual point-source fluorophores and tends to generate fluorescent “clusters” in the periphery which are artifacts of deconvolving noise

Article Snippet: Figure 2 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.

Techniques: Fluorescence, Microscopy

Journal: Cellular and Molecular Bioengineering

Article Title: Model Convolution: A Computational Approach to Digital Image Interpretation

doi: 10.1007/s12195-010-0101-7

Figure Lengend Snippet: 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

Article Snippet: Figure 2 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.

Techniques:

Journal: Cellular and Molecular Bioengineering

Article Title: Model Convolution: A Computational Approach to Digital Image Interpretation

doi: 10.1007/s12195-010-0101-7

Figure Lengend Snippet: The model-convolution method as compared to the image deconvolution process. In the image deconvolution process, an experimental image is “deblurred” using the theoretical microscope PSF. With the model-convolution method, a theoretical fluorophore distribution is convolved with the microscope PSF and noise, and a simulated image is generated. Thus, the model-convolution method is essentially the inverse of the image deconvolution process. (a) The example shown is a computational model of Arp2/3-mediated actin filament branching in three dimensions based on experimental observations by Ichetovkin et al . The model results in a branched actin filament structure stemming from an initial nucleation site (1—blue arrow) and leading to a series of branches off the main filament. The model-convolution method is applied to create a theoretical microscope image at the focal plane of the main filament. Branches that are close to the focal plane of the microscope (2—orange arrow) are clearly visible in the simulated fluorescence image. Branches that project out of the focal plane (3—red arrow, and 4—white arrow) are less visible in the simulated image, indicating that the branching complexity and branch length distribution of the actin filament could be misinterpreted from experimental fluorescence images. Scale bar, 1000 nm. (b) The model-convolution approach to estimating microtubule curvature. A simulated microtubule is constructed with a known analytical function (Sine function on a 2 nm pixel grid), showing the true underlying relation of the curvature to the outer diameter. This simulated microtubule has curvature that would be at the high extreme of observed curvatures in living cells. The model-convolution operation is performed, and the resulting image is binned to the pixel size associated with a high NA lens and ccd detector (50 nm pixel size). The convolved image appears more highly curved than the underlying filament, and the digitization on the camera makes quantitative analysis of curvature prone to errors. Scale bar, 250 nm

Article Snippet: Figure 2 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.

Techniques: Microscopy, Generated, Fluorescence, Construct