Mereotopological correction of segmentation errors in histological imaging

David A. Randell*, Antony Galton, Shereen Fouad, Hisham Mehanna, Gabriel Landini

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review


    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.

    Original languageEnglish
    Article number63
    JournalJournal of Imaging
    Issue number4
    Publication statusPublished - 12 Dec 2017

    Bibliographical note

    Funding Information:
    This work was supported by the EPSRC through funding under grant EP/M023869/1, “Novel context-based segmentation algorithms for intelligent microscopy”.


    • Graph theory
    • Histological image processing
    • Mereotopology


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