Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy images
IEEE Transactions on Medical Imaging
1121 - 1131
Item Usage Stats
MetadataShow full item record
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.
KeywordsAttributed relational graph
Attributed relational graph
Fluorescence microscopy imaging
Cell nucleus segmentation
Liver cell carcinoma
Published Version (Please cite this version)http://dx.doi.org/10.1109/TMI.2013.2255309
Showing items related by title, author, creator and subject.
Tosun, Akif Burak; Sokmensuer, C.; Gündüz-Demir, Çiğdem (IEEE, 2010)This paper presents a new algorithm for the unsupervised segmentation of tissue images. It relies on using the spatial information of cytological tissue components. As opposed to the previous study, it does not only use ...
Koyuncu, Can Fahrettin; Durmaz, İrem; Çetin-Atalay, Rengül; Gündüz-Demir, Çiğdem (IEEE Computer Society, 2014-04)Automated cell imaging systems have been proposed for faster and more reliable analysis of biological events at the cellular level. The first step of these systems is usually cell segmentation whose success affects the ...
Image segmentation with unified region and boundary characteristics within recursive shortest spanning tree Esen, E.; Alp, Yaşar Kemal (IEEE, 2007)The lack of boundary information in region based image segmentation algorithms resulted in many hybrid methods that integrate the complementary information sources of region and boundary, in order to increase the segmentation ...