Morphological separation of clustered nuclei in histological images
Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review
Authors
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 language | English |
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Title of host publication | 13th International Conference on Image Analysis and Recognition, ICIAR 2016, Póvoa de Varzim, Portugal, July 13-15, 2016. Proceedings |
Publication status | E-pub ahead of print - 1 Jul 2016 |
Event | 13th International Conference on Image Analysis and Recognition - Póvoa de Varzim, Portugal Duration: 13 Jul 2016 → 15 Jul 2016 |
Publication series
Name | Lecture Notes in Computer Science |
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ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 13th International Conference on Image Analysis and Recognition |
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Abbreviated title | ICIAR 2016 |
Country | Portugal |
City | Póvoa de Varzim |
Period | 13/07/16 → 15/07/16 |
Keywords
- Histological images, Nuclear segmentation, Mathematical morphology, Concavity analysis