Morphological Separation of Clustered Nuclei in Histological Images
Fouad, S; Landini, G; Randell, D; et al.Galton, AP
Date: 1 July 2016
Article, Conference paper
Journal
Lecture Notes in Computer Science
Publisher
Springer Verlag
Publisher DOI
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 ...
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.
Computer Science
Faculty of Environment, Science and Economy
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