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Generalized Pareto Distributions, Image Statistics and Autofocusing in Automated Microscopy
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0001-7557-4904
2015 (English)In: GEOMETRIC SCIENCE OF INFORMATION, GSI 2015, Springer-Verlag New York, 2015, 96-103 p.Conference paper (Refereed)
Abstract [en]

We introduce the generalized Pareto distributions as a statistical model to describe thresholded edge-magnitude image filter results. Compared to the more common Weibull or generalized extreme value distributions these distributions have at least two important advantages, the usage of the high threshold value assures that only the most important edge points enter the statistical analysis and the estimation is computationally more efficient since a much smaller number of data points have to be processed. The generalized Pareto distributions with a common threshold zero form a two-dimensional Riemann manifold with the metric given by the Fisher information matrix. We compute the Fisher matrix for shape parameters greater than -0.5 and show that the determinant of its inverse is a product of a polynomial in the shape parameter and the squared scale parameter. We apply this result by using the determinant as a sharpness function in an autofocus algorithm. We test the method on a large database of microscopy images with given ground truth focus results. We found that for a vast majority of the focus sequences the results are in the correct focal range. Cases where the algorithm fails are specimen with too few objects and sequences where contributions from different layers result in a multi-modal sharpness curve. Using the geometry of the manifold of generalized Pareto distributions more efficient autofocus algorithms can be constructed but these optimizations are not included here.

Place, publisher, year, edition, pages
Springer-Verlag New York, 2015. 96-103 p.
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 9389
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:liu:diva-127703DOI: 10.1007/978-3-319-25040-3_11ISI: 000374288700011ISBN: 978-3-319-25039-7ISBN: 978-3-319-25040-3OAI: diva2:926687
2nd International SEE Conference on Geometric Science of Information (GSI)
Swedish Research Council, 2014-6227Swedish Foundation for Strategic Research , IIS11-0081
Available from: 2016-05-09 Created: 2016-05-09 Last updated: 2016-05-30

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Lenz, Reiner
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Media and Information TechnologyFaculty of Science & EngineeringCenter for Medical Image Science and Visualization (CMIV)
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ReferencesLink to record
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