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Proceedings Paper

A fast method for approximate registration of whole-slide images of serial sections using local curvature
Author(s): Nicholas Trahearn; David Epstein; David Snead; Ian Cree; Nasir Rajpoot
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Paper Abstract

We present a method for fast, approximate registration of whole-slide images (WSIs) of histopathology serial sections. Popular histopathology slide registration methods in the existing literature tend towards intensity-based approaches.1, 2 Further input, in the form of an approximate initial transformation to be applied to one of the two WSIs, is then usually required, and this transformation needs to be optimised. Such a transformation is not readily available in this context and thus there is a need for fast approximation of these parameters. Fast registration is achieved by comparison of the external boundaries of adjacent tissue sections, using local curvature on multiple scales to assess similarity. Our representation of curvature is a modified version of the Curvature Scale Space (CSS)3 image. We substitute zero crossings with signed local absolute maxima of curvature to improve the registration's robustness to the subtle morphological differences of adjacent sections. A pairwise matching is made between curvature maxima at scales increasing exponentially, the matching minimizes the distance between maxima pairs at each scale. The boundary points corresponding to the matched maxima pairs are used to estimate the desired transformation. Our method is highly robust to translation, rotation, and linear scaling, and shows good performance in cases of moderate non-linear scaling. On our set of test images the algorithm shows improved reliability and processing speed in comparison to existing CSS based registration methods.

Paper Details

Date Published: 20 March 2014
PDF: 12 pages
Proc. SPIE 9041, Medical Imaging 2014: Digital Pathology, 90410E (20 March 2014); doi: 10.1117/12.2043308
Show Author Affiliations
Nicholas Trahearn, The Univ. of Warwick (United Kingdom)
David Epstein, The Univ. of Warwick (United Kingdom)
David Snead, Univ. Hospitals Coventry and Warwickshire (United Kingdom)
Ian Cree, The Univ. of Warwick (United Kingdom)
Univ. Hospitals Coventry and Warwickshire (United Kingdom)
Nasir Rajpoot, The Univ. of Warwick (United Kingdom)
Qatar Univ. (Qatar)

Published in SPIE Proceedings Vol. 9041:
Medical Imaging 2014: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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