Biomedical subjects

J A Conchello

Publications and source records attributed to J A Conchello.

8 recordsLinked to original sources

PubMed2001

Artefacts in restored images due to intensity loss in three-dimensional fluorescence microscopy.

Computational algorithms for three-dimensional deconvolution have proven successful in reducing blurring and improving the resolution of fluorescence microscopic images. However, discrepancies between the imaging conditions and the models on which such deconvolution algorithms are based may lead to artefacts and/or distortions in the images restored by application of the algorithms. In this paper, artefacts associated with a decrease of fluorescence intensity with time or slice in three-dimensional wide-field images are demonstrated using simulated images. Loss of intensity, whether due to photobleaching or other factors, leads to artefacts in the form of bands or stripes in the restored images. An empirical method for correcting the intensity losses in wide-field images has been implemented and used to correct biological images. This method is based on fitting a decreasing function to the slice intensity curve computed by summing all pixel values in each slice. The fitted curve is then used for the calculation of correction factors for each slice.

Algorithms↗

PubMed2001

Fast maximum-likelihood image-restoration algorithms for three-dimensional fluorescence microscopy.

We have evaluated three constrained, iterative restoration algorithms to find a fast, reliable algorithm for maximum-likelihood estimation of fluorescence microscopic images. Two algorithms used a Gaussian approximation to Poisson statistics, with variances computed assuming Poisson noise for the images. The third method used Csiszar's information-divergence (I-divergence) discrepancy measure. Each method included a nonnegativity constraint and a penalty term for regularization; optimization was performed with a conjugate gradient method. Performance of the methods was analyzed with simulated as well as biological images and the results compared with those obtained with the expectation-maximization-maximum-likelihood (EM-ML) algorithm. The I-divergence-based algorithm converged fastest and produced images similar to those restored by EM-ML as measured by several metrics. For a noiseless simulated specimen, the number of iterations required for the EM-ML method to reach a given log-likelihood value was approximately the square of the number required for the I-divergence-based method to reach the same value.

Algorithms↗

PubMed2000

From plaque to pretzel: fold formation and acetylcholine receptor loss at the developing neuromuscular junction.

Although there has been progress in understanding the initial steps in the formation of synapses, less is known about their subsequent maturation (Sanes and Lichtman, 1999). Two alterations on the postsynaptic side of the mammalian neuromuscular junction occur during early postnatal life: acetylcholine receptors (AChRs) disappear from parts of the developing junction as all but one axonal inputs are removed, and the topography of the postsynaptic membrane becomes more complicated as gutters and folds are established. We have studied the maturation of the AChR distribution and postsynaptic topography simultaneously by imaging labeled AChRs at the mouse neuromuscular junction in a new way, using reflected light confocal microscopy. At birth postsynaptic receptors were localized in irregular patches within a spoon-shaped plaque. Beginning several days later, receptor regions within a single endplate were divided into differentiated and less organized compartments. Folds generally oriented orthogonal to the long axis of the muscle fiber were seen in developing gutters, although the orientation of the gutters seemed to be imposed by the branching pattern of the nerve. Eventually, superficial regions lacking AChR labeling were apparent in all junctions. In junctions denervated in the neonatal period both gutter formation and the disappearance of superficial receptors regions were prevented. We suggest that tension between growing muscle fibers and the relatively inelastic synaptic terminals that adhere to them causes the topographic features of the postsynaptic membrane. This view provides a mechanical explanation for gutters, folds, and the location of folds at sites of neurotransmitter release.

Age Factors↗

PubMed1999

Three-dimensional imaging by deconvolution microscopy.

Deconvolution is a computational method used to reduce out-of-focus fluorescence in three-dimensional (3D) microscope images. It can be applied in principle to any type of microscope image but has most often been used to improve images from conventional fluorescence microscopes. Compared to other forms of 3D light microscopy, like confocal microscopy, the advantage of deconvolution microscopy is that it can be accomplished at very low light levels, thus enabling multiple focal-plane imaging of light-sensitive living specimens over long time periods. Here we discuss the principles of deconvolution microscopy, describe different computational approaches for deconvolution, and discuss interpretation of deconvolved images with a particular emphasis on what artifacts may arise.

Artifacts↗

PubMed1999

Theoretical development and experimental evaluation of imaging models for differential-interference-contrast microscopy.

Imaging models for differential-interference-contrast (DIC) microscopy are presented. Two- and three-dimensional models for DIC imaging under partially coherent illumination were derived and tested by using phantom specimens viewed with several conventional DIC microscopes and quasi-monochromatic light. DIC images recorded with a CCD camera were compared with model predictions that were generated by using theoretical point-spread functions, computer-generated phantoms, and estimated imaging parameters such as bias and shear. Results show quantitative and qualitative agreement between model and data for several imaging conditions.

Evaluation Studies as Topic↗

PubMed1999

Parametric blind deconvolution: a robust method for the simultaneous estimation of image and blur.

Blind-deconvolution microscopy, the simultaneous estimation of the specimen function and the point-spread function (PSF) of the microscope, is an underdetermined problem with nonunique solutions that are usually avoided by enforcing constraints on the specimen function and the PSF. We derived a maximum-likelihood-based method for blind deconvolution in which we assume a mathematical model for the PSF that depends on a small number of parameters (e.g., less than 20). The algorithm then estimates the unknown parameters together with the specimen function. The mathematical model ensures that all the constraints of the PSF are satisfied, and the maximum-likelihood approach ensures that the specimen is nonnegative. The method successfully estimates the PSF and removes out-of-focus blur. The PSF estimation is robust to aberrations in the PSF and to noise in the image.

Algorithms↗

PubMed1998

Superresolution and convergence properties of the expectation-maximization algorithm for maximum-likelihood deconvolution of incoherent images.

Computational optical-sectioning microscopy with a nonconfocal microscope is fundamentally limited because the optical transfer function, the Fourier transform of the point-spread function, is exactly zero over a conic region of the spatial-frequency domain. Because of this missing cone of optical information, images are potentially artifactual. To overcome this limitation, superresolution, in the sense of band extrapolation, is necessary. I present a frequency-domain analysis of the expectation-maximization algorithm for maximum-likelihood image estimation that shows how the algorithm achieves this band extrapolation. This analysis gives the theoretical absolute bandwidth of the restored image; however, this absolute value may not be realistic in many cases. Then a second analysis is presented that assumes a Gaussian point-spread function and a specimen function and shows more realistic behavior of the algorithm and demonstrates some of its properties. Experimental results on the superresolving capability of the algorithm are also presented.

Algorithms↗

PubMed1994

Artifacts in computational optical-sectioning microscopy.

We tested the most complete optical model available for computational optical-sectioning microscopy and obtained four main results. First, we observed good agreement between experimental and theoretical point-spread functions (PSF's) under a variety of imaging conditions. Second, using these PSF's, we found that a linear restoration method yielded reconstructed images of a well-defined phantom object (a 10-microns-diameter fluorescent bead) that closely resembled the theoretically determined, best-possible linear reconstruction of the object. Third, this best linear reconstruction suffered from a (to our knowledge) previously undescribed artifactual axial elongation whose principal cause was not increased axial blur but rather the conical shape of the null space intrinsic to nonconfocal three-dimensional (3D) microscopy. Fourth, when 10-microns phantom beads were embedded at different depths in a transparent medium, reconstructed bead images were progressively degraded with depth unless they were reconstructed with use of a PSF determined at the bead's depth. We conclude that (1) the optical model for optical sectioning is reasonably accurate; (2) if PSF shift variance cannot be avoided by adjustment of the optics, then reconstruction methods must be modified to account for this effect; and (3) alternative microscopical or nonlinear algorithmic approaches are required for overcoming artifacts imposed by the missing cone of frequencies that is intrinsic to nonconfocal 3D microscopy.

Artifacts↗