Model-based correction of segmentation errors in digitised histological images

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

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputConference publication

Abstract

This paper describes an application of topological, model-based methods for the algorithmic correction of segmentation errors in digitised histological images. The topological analysis is provided by the spatial logic Discrete Mereotopology and integrates qualitative spatial reasoning and constraint satisfaction methods with classical image processing methods. A set of eight topological relations defined on binary segmented regions are factored out and reworked as nodes of a set of directed graphs. The graphs encode and constrain a set of set-theoretic and topological segmentation operations on regions, so that the interpreted images and any proposed changes made to the regions can be made to conform to a valid histological model. Worked examples are given using images of H&E stained H400 cell line cultures.

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings
EditorsVictor Gonzalez-Castro, Maria Valdes Hernandez
PublisherSpringer
Pages718-730
Number of pages13
ISBN (Print)9783319609638
DOIs
Publication statusPublished - 22 Jun 2017
Event21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017 - Edinburgh, United Kingdom
Duration: 11 Jul 201713 Jul 2017

Publication series

NameCommunications in Computer and Information Science
Volume723
ISSN (Print)1865-0929

Conference

Conference21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017
Country/TerritoryUnited Kingdom
CityEdinburgh
Period11/07/1713/07/17

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”.

Keywords

  • Graph theory
  • Histological image processing
  • Mereotopology

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