Mereotopological Correction of Segmentation Errors in Histological Imaging

David Randell, Antony Galton, Shereen Fouad, Hisham Mehanna, and Gabriel Landini

In Journal of Imaging, Volume 3 number 4, 2017.
doi:10.3390/jimaging3040063

Abstract

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.

The work reported in this paper was funded by EPSRC grant EP/M023869/1, Novel context-based segmentation algorithms for intelligent microscopy

Published online 12th December 2017.


Antony Galton