Morphological Separation of Clustered Nuclei in Histological Images
Lecture Notes in Computer Science
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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.
The research reported in this paper was supported by the Engineering and Physical Sciences Research Council (EPSRC), UK through funding under grant EP/M023869/1 “Novel context-based segmentation algorithms for intelligent microscopy”
13th International Conference, ICIAR 2016, in Memory of Mohamed Kamel, Póvoa de Varzim, Portugal, July 13-15, 2016
This is the final version of the article. Available from Springer Verlag via the DOI in this record.
Vol. 9730 (Image Analysis and Recognition), pp. 599-607