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Proceedings Paper

Nearly rigid descriptor-based matching for volume reconstruction from histological sections
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Paper Abstract

A common task in the analysis of digitized histological sections is reconstructing a volumetric representation of the original specimen. Image registration algorithms are used in this task to compensate for translational, rotational, scale, shear, and local geometric differences between slices. Various systems have been developed to perform volumetric reconstruction by registering pairs of successive slices according to rigid, similarity, affine, and/or deformable transformations. To provide a coarse initial volumetric reconstruction, rigid transformations may be too constrained, as they do not allow for scale or shear; but, affine transformations may be too flexible, enabling larger scale or shear factors than physically reflected in the histological sections. One difficulty with these systems is caused by the aperture problem; even if successive slices are registered reasonably well, the composition of transformations over tens or hundreds of slices can yield global twisting and scale and shear changes that yield a volumetric reconstruction that is significantly distorted from the shape of the true specimen. The impact of the aperture problem can be reduced by considering more than two successive images in the registration process. Systems that take this approach use global energy functions, elastic spring models, post hoc filtering/smoothing, or solutions to shortest-path problems on graphs. In this article, we propose a volume reconstruction algorithm that handles the aperture problem and yields nearly rigid transformations (i.e., affine transformations with small scale and shear factors). Our algorithm is based on robust geometric alignment of descriptive feature points (for example, using SIFT16) via constrained optimization. We will illustrate our algorithm on the task of volumetric reconstruction from histological sections of a chicken embryo with an embedded tumor spheroid.

Paper Details

Date Published: 14 February 2012
PDF: 9 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831417 (14 February 2012); doi: 10.1117/12.911665
Show Author Affiliations
Shaohui Sun, Rochester Institute of Technology (United States)
Nzola De Magalhães, Rochester Institute of Technology (United States)
Nathan D. Cahill, Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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