Maximum-likelihood reconstruction of 3D confocal data sets

Spyridon S. Stefanou; Eric W. Hansen

doi:10.1117/12.172092

4 April 1994 Maximum-likelihood reconstruction of 3D confocal data sets

Spyridon S. Stefanou, Eric W. Hansen

Author Affiliations +

Proceedings Volume 2184, Three-Dimensional Microscopy: Image Acquisition and Processing; (1994) https://doi.org/10.1117/12.172092
Event: IS&T/SPIE 1994 International Symposium on Electronic Imaging: Science and Technology, 1994, San Jose, CA, United States

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.

Citation Download Citation

Spyridon S. Stefanou and Eric W. Hansen "Maximum-likelihood reconstruction of 3D confocal data sets", Proc. SPIE 2184, Three-Dimensional Microscopy: Image Acquisition and Processing, (4 April 1994); https://doi.org/10.1117/12.172092

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