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Proceedings Paper

A least squares approach to estimating the probability distribution of unobserved data in multiphoton microscopy
Author(s): Paul Salama
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Paper Abstract

Multi-photon microscopy has provided biologists with unprecedented opportunities for high resolution imaging deep into tissues. Unfortunately deep tissue multi-photon microscopy images are in general noisy since they are acquired at low photon counts. To aid in the analysis and segmentation of such images it is sometimes necessary to initially enhance the acquired images. One way to enhance an image is to find the maximum a posteriori (MAP) estimate of each pixel comprising an image, which is achieved by finding a constrained least squares estimate of the unknown distribution. In arriving at the distribution it is assumed that the noise is Poisson distributed, the true but unknown pixel values assume a probability mass function over a finite set of non-negative values, and since the observed data also assumes finite values because of low photon counts, the sum of the probabilities of the observed pixel values (obtained from the histogram of the acquired pixel values) is less than one. Experimental results demonstrate that it is possible to closely estimate the unknown probability mass function with these assumptions.

Paper Details

Date Published: 26 February 2008
PDF: 10 pages
Proc. SPIE 6814, Computational Imaging VI, 68140U (26 February 2008); doi: 10.1117/12.764919
Show Author Affiliations
Paul Salama, Indiana Univ.-Purdue Univ. at Indianapolis (United States)

Published in SPIE Proceedings Vol. 6814:
Computational Imaging VI
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)

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