Segmentation of overlapping macrophages using Anglegram analysis

José Alonso Solís-Lemus*, Brian Stramer, Greg Slabaugh, Constantino Carlos Reyes-Aldasoro

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingOther chapter contributionpeer-review

1 Citation (Scopus)


This paper describes the automatic segmentation of overlapping cells through different algorithms. As the first step, the algorithm detects junctions between the boundaries of overlapping objects based on the angles between points of the overlapping boundary. For this purpose, a novel 2D matrix with multiscale angle variation is introduced, i.e. anglegram. The anglegram is used to find junctions of overlapping cells. The algorithm to retrieve junctions from the boundary was tested and validated with synthetic data and fluorescently labelled macrophages observed on embryos of Drosophila melanogaster. Then, four different segmentation techniques were evaluated: (i) a Voronoi partition based on the nuclei positions, (ii) a slicing method, which joined the clumps together (junction slicing), (iii) a partition based on the following of the edges from the junctions (edge following), and (iv) a custom self-organising map to fit to the area of overlap between the cells. Only (ii)-(iv) were based on the junctions. The segmentation results were compared based on precision, recall and Jaccard similarity. The algorithm that reported the best segmentation was the junction slicing.

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319609638
Publication statusE-pub ahead of print - 22 Jun 2017
Event21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017 - Edinburgh, United Kingdom
Duration: 11 Jul 201713 Jul 2017

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)18650929


Conference21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017
Country/TerritoryUnited Kingdom


  • Macrophages
  • Overlapping objects
  • Segmentation
  • Self-organising maps


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