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J. Imaging 2017, 3(4), 63; doi:10.3390/jimaging3040063

Mereotopological Correction of Segmentation Errors in Histological Imaging

School of Dentistry, College of Medical and Dental Sciences, University of Birmingham, Birmingham B152TT, UK
Department of Computer Science, University of Exeter, Exeter EX4, UK
Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B152TT, UK
This paper is an extended version of our paper published in: Randell, D.A.; Galton, A.; Fouad, S.; Mehanna, H.; Landini, G. Model-based Correction of Segmentation Errors in Digitised Histological Images. In Communications in Computer and Information Science, Proceedings of the Medical Image Understanding and Analysis. (MIUA), Edinburgh, UK, 11–13 July 2017; Valdés Hernández, M., González-Castro, V., Eds.; Springer: Cham, Switzerland, 2017; Volume 723, pp. 718–730.
Author to whom correspondence should be addressed.
Received: 30 October 2017 / Revised: 5 December 2017 / Accepted: 6 December 2017 / Published: 12 December 2017
(This article belongs to the Special Issue Selected Papers from “MIUA 2017”)
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In this paper we describe mereotopological methods to programmatically correct image segmentation errors, in particular those that fail to fulfil expected spatial relations in digitised histological scenes. The proposed approach exploits a spatial logic called discrete mereotopology to integrate a number of qualitative spatial reasoning and constraint satisfaction methods into imaging procedures. Eight mereotopological relations defined on binary region pairs are represented as nodes in a set of 20 directed graphs, where the node-to-node graph edges encode the possible transitions between the spatial relations after set-theoretic and discrete topological operations on the regions are applied. The graphs allow one to identify sequences of operations that applied to regions of a given relation, and enables one to resegment an image that fails to conform to a valid histological model into one that does. Examples of the methods are presented using images of H&E-stained human carcinoma cell line cultures. View Full-Text
Keywords: mereotopology; graph theory; histological image processing mereotopology; graph theory; histological image processing

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Randell, D.A.; Galton, A.; Fouad, S.; Mehanna, H.; Landini, G. Mereotopological Correction of Segmentation Errors in Histological Imaging. J. Imaging 2017, 3, 63.

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