Ontological levels in histological imaging

Antony Galton*, Gabriel Landini, David Randell, Shereen Fouad

*Corresponding author for this work

    Research output: Chapter in Book/Published conference outputConference publication


    In this paper we present an ontological perspective on ongoing work in histological and histopathological imaging involving the quantitative and algorithmic analysis of digitised images of cells and tissues. We present the derivation of consistent histological models from initially captured images of prepared tissue samples as a progression through a number of ontological levels, each populated by its distinctive classes of entities related in systematic ways to entities at other levels. We see this work as contributing to ongoing efforts to provide a consistent and widely accepted suite of ontological resources such as those currently constituting the OBO Foundry, and where possible we draw links between our work and existing ontologies within that suite.

    Original languageEnglish
    Title of host publicationFormal Ontology in Information Systems - Proceedings of the 9th International Conference, FOIS 2016
    EditorsRoberta Ferrario, Werner Kuhn
    Number of pages14
    ISBN (Electronic)9781614996590
    Publication statusPublished - 2016
    Event9th Formal Ontology in Information Systems Conference, FOIS 2016 - Annecy, France
    Duration: 6 Jul 20169 Jul 2016

    Publication series

    NameFrontiers in Artificial Intelligence and Applications
    ISSN (Print)0922-6389


    Conference9th Formal Ontology in Information Systems Conference, FOIS 2016

    Bibliographical note

    Funding Information:
    This research is supported by EPSRC through funding under grant EP/M023869/1 "Novel context-based segmentation algorithms for intelligent microscopy".


    • Digitised images
    • Discrete mereotopology
    • Formal ontologies
    • Histological image processing
    • Histological stains
    • Histology
    • Histopathology
    • Mathematical morphology
    • Microscopy
    • Segmentation
    • Spatial logics


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