Three-dimensional quasi-binary image restoration for confocal microscopy and its application to dendritic trees
For the analysis of learning processes and the underlying changes of the shape of excitatory synapses (spines), 3-D volume samples of selected dendritic segments are scanned by a confocal laser scanning microscope. The images are unsharp because of the (direction dependent) resolution limit. A simple deconvolution is not sufficient for the needed resolution.
Therefore parametric model for the dendrite and the spines is created to reconstruct structures and edge positions with a resolution smaller than one voxel. The tree-like structure of the nerve cell serves as a-priori information. Simple geometrical elements are connected to a model that is adapted for size and position in sub-pixel domain. To estimate the deviation between the microscope image and the model, the model is sampled with the same resolution as the microscope image and convolved by the microscope point spread function (PSF). During an iterative process the model parameters are optimised. The result is a binary image of higher resolution without strong distortions by PSF.
KeywordsPoint Spread Function Excitatory Synapse Element Point Dendritic Segment Average Gray Level
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- 1.H. Scheich, E. WallhÄaußer-Franke, K. Braun, “Does synaptic selection explain auditory imprinting?” Memory: Organization and Locus of Change, Oxford University Press, pp. 114–159, 1991.Google Scholar
- 3.Imaris 2.2.4 Reference Manual, Bitplan AG, Technopark Zürich, Technoparkstrasse 30, 8005 Zürich, Switzerland 1994.Google Scholar
- 4.J. B. Pawley (editor), Handbook of Biological Confocal Microscopy. Plenum Press, New York, revised edition 1989.Google Scholar
- 5.H. T. M. van der Voort, K. C. Strasters: Restoration of Confocal Images for Quantitative Image Analysis. Journal of Microscopy vol. 178, pp. 165–181, 1995.Google Scholar
- 6.A. Herzog; G. Sommerkorn; U. Seiffert; B. Michaelis; K. Braun; W. Zuschratter, “Rekonstruktion und Klassifikation dendritischer Spines aus konfokalen Bilddaten” Proceedings des Aachener Workshops „Bildverarbeitung in der Medizin 08.11–09.11.1996”, Springer, pp. 65–70, 1996.Google Scholar
- 7.G. Sommerkorn, U. Seiffert, D. Surmeli, A. Herzog, B. Michaelis, K. Braun.: Classification of 3D dendritic Spines using SOM. International Conference of Artificial Neural Networks and Genetic Algorithms (ICANNGA97), Norwich, England 2.4.–4.4. 1997.Google Scholar