Paper
15 May 2002 2D and 3D deconvolution of confocal fluorescence images by maximum likelihood estimation
Jeffrey M. Larson
Author Affiliations +
Abstract
Deconvolution of confocal fluorescence images by maximum likelihood estimation (MLE) was investigated for its ability to increase the information content in the images. 3-D MLE algorithms, applied to confocal image stacks, increase lateral and axial resolution and result in a finer optical section. Contrast, especially at edges, is enhanced, improving the documentation quality of high magnification images beyond that possible by confocal microscopy alone. Axial smear associated with spherical aberration was not removed by deconvolution and a lateral thinning artifact was introduced. Single confocal images can be rapidly deconvolved by 2-D MLE by applying a two-dimensional point spread function and treating them as images of planar objects. The 2-D algorithm can also deconvolve a maximum projection of a stack. The method works best when there is a minimal overlap of fluorescent structures. The projection is treated as a two dimensional object. Intensify information excluded by the projection operation cannot be recovered. Deconvolution of images acquired with the pinhole opened to increase sensitivity closely matches images acquired through an optimal opening, although in 2-D MLE, colocalization cannot be distinguished from overlap and the integrity of quantitative data cannot be guaranteed. Properly applied, MLE deconvolution increases the useful information content of confocal images.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jeffrey M. Larson "2D and 3D deconvolution of confocal fluorescence images by maximum likelihood estimation", Proc. SPIE 4621, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing IX, (15 May 2002); https://doi.org/10.1117/12.467835
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Cited by 5 scholarly publications.
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KEYWORDS
Deconvolution

Confocal microscopy

Luminescence

3D image processing

Point spread functions

Objectives

Image resolution

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