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Proceedings Paper

Maximum-likelihood reconstruction of 3D confocal data sets
Author(s): Spyridon S. Stefanou; Eric W. Hansen
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Paper Abstract

Even in confocal scanning, longitudinal resolution is poorer than lateral resolution. It is therefore of interest to go `beyond confocal' and achieve still better optical sectioning by image restoration methods. In our previous work we applied two methods to simulated 3D microscope images: the constrained Jansson-van Cittert (JVC) method, which is a deterministic regularized image restoration algorithm, and the expectation-maximization (EM) algorithm, which is a method to obtain the maximum likelihood solution of the restoration problem under Poisson image statistics. In this paper we apply both the JVC algorithm and the EM algorithm to real image data obtained from our laser scanning confocal microscope. Slices of the original and restored images agree with our earlier numerical simulations. Specifically: (a) optical sectioning is improved by both algorithms; (b) the JVC restoration is noisier than the image restored with the EM algorithm, showing the advantage of the ML approach under low light conditions; (c) noise in the EM restoration shows that regularization is still needed.

Paper Details

Date Published: 4 April 1994
PDF: 6 pages
Proc. SPIE 2184, Three-Dimensional Microscopy: Image Acquisition and Processing, (4 April 1994); doi: 10.1117/12.172092
Show Author Affiliations
Spyridon S. Stefanou, Dartmouth College (United States)
Eric W. Hansen, Dartmouth College (United States)

Published in SPIE Proceedings Vol. 2184:
Three-Dimensional Microscopy: Image Acquisition and Processing
Carol J. Cogswell; Kjell Carlsson, Editor(s)

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