Morphological separation of clustered nuclei in histological images

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

Colleges, School and Institutes

Abstract

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.

Details

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

Conference

Conference13th International Conference on Image Analysis and Recognition
Abbreviated titleICIAR 2016
CountryPortugal
CityPóvoa de Varzim
Period13/07/1615/07/16

Keywords

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