Novel applications of discrete mereotopology to mathematical morphology

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This paper shows how the Discrete Mereotopology notions of adjacency and neighbourhood between regions can be exploited through Mathematical Morphology to accept or reject changes resulting from traditional morphological operations such as closing and opening. This leads to a set of six morphological operations (here referred to generically as minimal opening and minimal closing)where minimal changes fulfil specific spatial constraints. We also present an algorithm to compute the RCC5D and RCC8D relation sets across multiple regions resulting in a performance improvement of over three orders of magnitude over our previously published algorithm for Discrete Mereotopology.

Original languageEnglish
Pages (from-to)109-117
Number of pages9
JournalSignal Processing: Image Communication
Early online date27 Apr 2019
Publication statusPublished - Aug 2019

Bibliographical note

Funding Information:
The research reported in this paper was supported by the Engineering and Physical Sciences Research Council (EPSRC) , UK through funding under grant EP/M023869/1 ‘Novel context-based segmentation algorithms for intelligent microscopy’.


  • Discrete mereotopology
  • Image processing
  • Mathematical morphology
  • Spatial reasoning


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