Morphological separation of clustered nuclei in histological images

Shereen Fouad; Gabriel Landini; David Randell; Antony Galton

doi:10.1007/978-3-319-41501-7_67

Morphological separation of clustered nuclei in histological images

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

^*Corresponding author for this work

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

Abstract

Automated nuclear segmentation is essential in the analysis of most microscopy images. This paper presents a novel concavitybased method for the separation of clusters of nuclei in binary images. A heuristic rule, based on object size, is used to infer the existence of merged regions. Concavity extrema detected along the merged-cluster boundary are used to guide the separation of overlapping regions. Inner split contours of multiple concavities along the nuclear boundary are estimated via a series of morphological procedures. The algorithm was evaluated on images of H400 cells in monolayer cultures and compares favourably with the state-of-art watershed method commonly used to separate overlapping nuclei.

Original language	English
Title of host publication	Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings
Editors	Aurelio Campilho, Aurelio Campilho, Fakhri Karray
Publisher	Springer
Pages	599-607
Number of pages	9
ISBN (Print)	9783319415000
DOIs	https://doi.org/10.1007/978-3-319-41501-7_67
Publication status	Published - 1 Jul 2016
Event	13th International Conference on Image Analysis and Recognition, ICIAR 2016 - Povoa de Varzim, Portugal Duration: 13 Jul 2016 → 16 Jul 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9730
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Image Analysis and Recognition, ICIAR 2016
Country/Territory	Portugal
City	Povoa de Varzim
Period	13/07/16 → 16/07/16

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”.

Keywords

Concavity analysis
Histological images
Mathematical morphology
Nuclear segmentation

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10.1007/978-3-319-41501-7_67Licence: CC BY 3.0

Cite this

Fouad, S., Landini, G., Randell, D., & Galton, A. (2016). Morphological separation of clustered nuclei in histological images. In A. Campilho, A. Campilho, & F. Karray (Eds.), Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings (pp. 599-607). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9730). Springer. https://doi.org/10.1007/978-3-319-41501-7_67

Fouad, Shereen ; Landini, Gabriel ; Randell, David et al. / Morphological separation of clustered nuclei in histological images. Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings. editor / Aurelio Campilho ; Aurelio Campilho ; Fakhri Karray. Springer, 2016. pp. 599-607 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Morphological separation of clustered nuclei in histological images",

abstract = "Automated nuclear segmentation is essential in the analysis of most microscopy images. This paper presents a novel concavitybased method for the separation of clusters of nuclei in binary images. A heuristic rule, based on object size, is used to infer the existence of merged regions. Concavity extrema detected along the merged-cluster boundary are used to guide the separation of overlapping regions. Inner split contours of multiple concavities along the nuclear boundary are estimated via a series of morphological procedures. The algorithm was evaluated on images of H400 cells in monolayer cultures and compares favourably with the state-of-art watershed method commonly used to separate overlapping nuclei.",

keywords = "Concavity analysis, Histological images, Mathematical morphology, Nuclear segmentation",

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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”. ; 13th International Conference on Image Analysis and Recognition, ICIAR 2016 ; Conference date: 13-07-2016 Through 16-07-2016",

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doi = "10.1007/978-3-319-41501-7_67",

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Fouad, S, Landini, G, Randell, D & Galton, A 2016, Morphological separation of clustered nuclei in histological images. in A Campilho, A Campilho & F Karray (eds), Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9730, Springer, pp. 599-607, 13th International Conference on Image Analysis and Recognition, ICIAR 2016, Povoa de Varzim, Portugal, 13/07/16. https://doi.org/10.1007/978-3-319-41501-7_67

Morphological separation of clustered nuclei in histological images. / Fouad, Shereen; Landini, Gabriel; Randell, David et al.
Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings. ed. / Aurelio Campilho; Aurelio Campilho; Fakhri Karray. Springer, 2016. p. 599-607 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9730).

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

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T1 - Morphological separation of clustered nuclei in histological images

AU - Fouad, Shereen

AU - Landini, Gabriel

AU - Randell, David

AU - Galton, Antony

N1 - 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”.

PY - 2016/7/1

Y1 - 2016/7/1

N2 - Automated nuclear segmentation is essential in the analysis of most microscopy images. This paper presents a novel concavitybased method for the separation of clusters of nuclei in binary images. A heuristic rule, based on object size, is used to infer the existence of merged regions. Concavity extrema detected along the merged-cluster boundary are used to guide the separation of overlapping regions. Inner split contours of multiple concavities along the nuclear boundary are estimated via a series of morphological procedures. The algorithm was evaluated on images of H400 cells in monolayer cultures and compares favourably with the state-of-art watershed method commonly used to separate overlapping nuclei.

AB - Automated nuclear segmentation is essential in the analysis of most microscopy images. This paper presents a novel concavitybased method for the separation of clusters of nuclei in binary images. A heuristic rule, based on object size, is used to infer the existence of merged regions. Concavity extrema detected along the merged-cluster boundary are used to guide the separation of overlapping regions. Inner split contours of multiple concavities along the nuclear boundary are estimated via a series of morphological procedures. The algorithm was evaluated on images of H400 cells in monolayer cultures and compares favourably with the state-of-art watershed method commonly used to separate overlapping nuclei.

KW - Concavity analysis

KW - Histological images

KW - Mathematical morphology

KW - Nuclear segmentation

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BT - Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings

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ER -

Fouad S, Landini G, Randell D, Galton A. Morphological separation of clustered nuclei in histological images. In Campilho A, Campilho A, Karray F, editors, Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings. Springer. 2016. p. 599-607. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-41501-7_67

Morphological separation of clustered nuclei in histological images

Abstract

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Bibliographical note

Keywords

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