Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.651858
Title: Automatic reconstruction from serial sections
Author: Guest, E.
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 1995
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Abstract:
In many experiments in biological and medical research, serial sectioning of biological material is the only way to reveal the three-dimensional (3D) structure and function. Many authors have reported how serial sections can be registered by means of fiducial markers or otherwise, but there have been only a few studies of automated correction of the sectioning distortions. In this thesis solutions to the registration problem are reviewed and discussed, and a solution to the warping problem, based on image processing techniques and the finite element method (FEM), is presented. The aim of this project was to develop a fully automatic method of reconstruction in order to provide a 3D atlas of mouse development as part of a gene expression database. For this purpose it is not necessary to warp the object so that it is identical to the original object, but to correct local distortions in the sections in order to produce a smooth representative mouse embryo. Furthermore, the use of fiducial markers was not possible because the reconstructions were from already sectioned material. In this thesis we demonstrate a new method for warping serial sections. The sections are warped by applying forces to each section, where each section is modelled as a thin elastic plate. The deformation forces are determined from correspondence between sections which are calculated by combining match strengths and positional information. The equilibrium state which represents the reconstructed 3D image is calculated using the finite element method. Results of the application of these methods to paraffin wax and resin embedded sections of the mouse embryo are presented.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.651858  DOI: Not available
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