Morphological separation of clustered nuclei in histological images

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Colleges, School and Institutes


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 publication13th International Conference on Image Analysis and Recognition, ICIAR 2016, Póvoa de Varzim, Portugal, July 13-15, 2016. Proceedings
Publication statusE-pub ahead of print - 1 Jul 2016
Event13th International Conference on Image Analysis and Recognition - Póvoa de Varzim, Portugal
Duration: 13 Jul 201615 Jul 2016

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Conference on Image Analysis and Recognition
Abbreviated titleICIAR 2016
CityPóvoa de Varzim


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