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


    Dive into the research topics of 'Morphological separation of clustered nuclei in histological images'. Together they form a unique fingerprint.

    Cite this