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Proceedings Paper

Transmission confocal microscopy: making it a reality
Author(s): Matthew R. Arnison; Pal W. Fekete; Marcos Serrano; Phuong Thuc Anh Nguyen; Raniero Romagnoli; Ling Guan; John W. O'Byrne; Carol J. Cogswell
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Paper Abstract

Imaging thick specimens in 3D transmission confocal modes presents two key problems. The first problem is variable aberrations introduced by changes in refractive index. The second problem is revealed when visualizing acquired data, where thick 3D datasets are difficult to interpret. In this paper we present our emerging solutions to these problems. Aberrations can be classified as simple tip-tilt deflection of the beam, or more complicated higher order aberrations. We discuss our results which demonstrate successful on-the- fly detection and correction for tip-tilt. For detecting higher order aberrations, we have chosen to investigate the wavefront curvature sensing technique. The second problem of rendering thick 3D datasets can be solved by extracting features of interest from the background. Simple intensity thresholding is not sufficient for complex biological specimens. And image processing in only 2D neglects any 3D structure. Use of Kohonen's self-organizing map neural network in 3D results in clear segmentation of features for sample chromosome specimens.

Paper Details

Date Published: 9 June 1998
PDF: 10 pages
Proc. SPIE 3261, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing V, (9 June 1998); doi: 10.1117/12.310536
Show Author Affiliations
Matthew R. Arnison, Univ. of Sydney (Australia)
Pal W. Fekete, Univ. of Sydney (Australia)
Marcos Serrano, Univ. of Sydney (Australia)
Phuong Thuc Anh Nguyen, Univ. of Sydney (Australia)
Raniero Romagnoli, Univ. of Sydney (Australia)
Ling Guan, Univ. of Sydney (Canada)
John W. O'Byrne, Univ. of Sydney (Australia)
Carol J. Cogswell, Univ. of Sydney (United States)


Published in SPIE Proceedings Vol. 3261:
Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing V
Thomas Taiwei Lu; Carol J. Cogswell; Jeremy M. Lerner; Jose-Angel Conchello; Jeremy M. Lerner; Thomas Taiwei Lu; Tony Wilson, Editor(s)

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