Morphological Separation of Clustered Nuclei in Histological Images

Shereen Fouad, Gabriel Landini, David Randell, and Antony Galton,

In Aurélio Campilho and Fakhri Karray (eds.), Proceedings of the 13th International Conference on Image Analysis and Recognition (ICIAR 2016), Póvoa de Varzim, Portugal, July 13-15, 2016, pages 599-607.
ISBN 978-3-319-41500-0

Abstract

Automated nuclear segmentation is essential in the analysis of most microscopy images. This paper presents a novel concavity-based 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.

Full paper (Open Access)

The work reported in this paper was funded by EPSRC grant EP/M023869/1, Novel context-based segmentation algorithms for intelligent microscopy

Antony Galton