Model-based correction of segmentation errors in digitised histological images

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

doi:10.1007/978-3-319-60964-5_63

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

Computer Science

Research output: Chapter in Book/Published conference output › Conference 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 language	English
Title of host publication	Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings
Editors	Victor Gonzalez-Castro, Maria Valdes Hernandez
Publisher	Springer
Pages	718-730
Number of pages	13
ISBN (Print)	9783319609638
DOIs	https://doi.org/10.1007/978-3-319-60964-5_63
Publication status	Published - 22 Jun 2017
Event	21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017 - Edinburgh, United Kingdom Duration: 11 Jul 2017 → 13 Jul 2017

Publication series

Name	Communications in Computer and Information Science
Volume	723
ISSN (Print)	1865-0929

Conference

Conference	21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017
Country/Territory	United Kingdom
City	Edinburgh
Period	11/07/17 → 13/07/17

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

Access to Document

10.1007/978-3-319-60964-5_63Licence: CC BY 3.0

Cite this

Randell, D. A., Galton, A., Fouad, S., Mehanna, H., & Landini, G. (2017). Model-based correction of segmentation errors in digitised histological images. In V. Gonzalez-Castro, & M. Valdes Hernandez (Eds.), Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings (pp. 718-730). (Communications in Computer and Information Science; Vol. 723). Springer. https://doi.org/10.1007/978-3-319-60964-5_63

Randell, David A. ; Galton, Antony ; Fouad, Shereen et al. / Model-based correction of segmentation errors in digitised histological images. Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings. editor / Victor Gonzalez-Castro ; Maria Valdes Hernandez. Springer, 2017. pp. 718-730 (Communications in Computer and Information Science).

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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.",

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author = "Randell, {David A.} and Antony Galton and Shereen Fouad and Hisham Mehanna and Gabriel Landini",

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”. ; 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017 ; Conference date: 11-07-2017 Through 13-07-2017",

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Randell, DA, Galton, A, Fouad, S, Mehanna, H & Landini, G 2017, Model-based correction of segmentation errors in digitised histological images. in V Gonzalez-Castro & M Valdes Hernandez (eds), Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings. Communications in Computer and Information Science, vol. 723, Springer, pp. 718-730, 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017, Edinburgh, United Kingdom, 11/07/17. https://doi.org/10.1007/978-3-319-60964-5_63

Model-based correction of segmentation errors in digitised histological images. / Randell, David A.; Galton, Antony; Fouad, Shereen et al.

Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings. ed. / Victor Gonzalez-Castro; Maria Valdes Hernandez. Springer, 2017. p. 718-730 (Communications in Computer and Information Science; Vol. 723).

Research output: Chapter in Book/Published conference output › Conference publication

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T1 - Model-based correction of segmentation errors in digitised histological images

AU - Randell, David A.

AU - Galton, Antony

AU - Fouad, Shereen

AU - Mehanna, Hisham

AU - Landini, Gabriel

N1 - Funding Information: This work was supported by the EPSRC through funding under grant EP/M023869/1, “Novel context-based segmentation algorithms for intelligent microscopy”.

PY - 2017/6/22

Y1 - 2017/6/22

N2 - 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.

AB - 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.

KW - Graph theory

KW - Histological image processing

KW - Mereotopology

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BT - Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings

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Randell DA, Galton A, Fouad S, Mehanna H, Landini G. Model-based correction of segmentation errors in digitised histological images. In Gonzalez-Castro V, Valdes Hernandez M, editors, Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings. Springer. 2017. p. 718-730. (Communications in Computer and Information Science). doi: 10.1007/978-3-319-60964-5_63

Model-based correction of segmentation errors in digitised histological images

Abstract

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Bibliographical note

Keywords

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