Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy images
Date
2013Source Title
IEEE Transactions on Medical Imaging
Print ISSN
0278-0062
Publisher
IEEE
Volume
32
Issue
6
Pages
1121 - 1131
Language
English
Type
ArticleItem Usage Stats
151
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views
148
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Abstract
More rapid and accurate high-throughput screening in molecular cellular biology research has become possible with the development of automated microscopy imaging, for which cell nucleus segmentation commonly constitutes the core step. Although several promising methods exist for segmenting the nuclei of monolayer isolated and less-confluent cells, it still remains an open problem to segment the nuclei of more-confluent cells, which tend to grow in overlayers. To address this problem, we propose a new model-based nucleus segmentation algorithm. This algorithm models how a human locates a nucleus by identifying the nucleus boundaries and piecing them together. In this algorithm, we define four types of primitives to represent nucleus boundaries at different orientations and construct an attributed relational graph on the primitives to represent their spatial relations. Then, we reduce the nucleus identification problem to finding predefined structural patterns in the constructed graph and also use the primitives in region growing to delineate the nucleus borders. Working with fluorescence microscopy images, our experiments demonstrate that the proposed algorithm identifies nuclei better than previous nucleus segmentation algorithms. © 2012 IEEE.
Keywords
Attributed relational graphGraph
Model-based segmentation
Nucleus segmentation
Attributed relational graph
Fluorescence microscopy imaging
Cytology
Monolayers
Image segmentation
Accuracy
Algorithm
Cell maturation
Cell nucleus
Cell nucleus segmentation
Cellular distribution
Comparative study
Fluorescence microscopy
Human
Human cell
Liver cell carcinoma
Algorithms
Permalink
http://hdl.handle.net/11693/20933Published Version (Please cite this version)
http://dx.doi.org/10.1109/TMI.2013.2255309Collections
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