Reference : Automated nuclei clump splitting by combining local concavity orientation and graph p...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Life sciences : Multidisciplinary, general & others
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/19047
Automated nuclei clump splitting by combining local concavity orientation and graph partitioning
English
Samsi, Siddharth mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Trefois, Christophe mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Antony, Paul mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Skupin, Alexander mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
2014
International Conference on Biomedical and Health Informatics
IEEE
412-415
Yes
No
International
978-1-4799-2131-7
2014 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
from 1-4 June 2014
IEEE-EMBS
Valencia
Italy
[en] Parkinson's disease ; Microscopy ; Cell segmentation ; Image segmentation
[en] Automated clump decomposition is essential for single cell based analysis of fluorescent microscopy images. This paper presents a new method for automatically splitting clumps of cell nuclei in fluorescence microscopy images. Nuclei are first segmented using histogram concavity analysis. Clumps of nuclei are detected by fitting an ellipse to the segmented objects and examining objects where the fitted ellipse does not overlap accurately with the segmented object. These clumps are then further processed to find concave points on the object boundaries. The orientation of the detected concavities is subsequently calculated based on the local shape of the object border. Finally, a graph segmentation based approach is used to pair concavities that represent best candidates for splitting touching nuclei based on properties derived from the local concavity properties. This approach was validated by manual inspection and has shown promising results in the high throughput analysis of HeLa cell images.
Luxembourg Centre for Systems Biomedicine (LCSB): Experimental Neurobiology (Balling Group)
University of Luxembourg - UL
http://hdl.handle.net/10993/19047
10.1109/BHI.2014.6864390

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