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 outputConference publication


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 languageEnglish
Title of host publicationImage Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings
EditorsAurelio Campilho, Aurelio Campilho, Fakhri Karray
Number of pages9
ISBN (Print)9783319415000
Publication statusPublished - 1 Jul 2016
Event13th International Conference on Image Analysis and Recognition, ICIAR 2016 - Povoa de Varzim, Portugal
Duration: 13 Jul 201616 Jul 2016

Publication series

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


Conference13th International Conference on Image Analysis and Recognition, ICIAR 2016
CityPovoa de Varzim

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”.


  • Concavity analysis
  • Histological images
  • Mathematical morphology
  • Nuclear segmentation


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