ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2010
Interactive segmentation of clustered cells via geodesic commute distance and constrained density weighted Nyström method.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Disentangling juxtacrine from paracrine signalling in dynamic tissue.PLoS computational biology · 2019Article
- Temperature regulates NF-κB dynamics and function through timing of A20 transcription.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Long-term tracking of budding yeast cells in brightfield microscopy: CellStar and the Evaluation Platform.Journal of the Royal Society, Interface · 2017Article
- Understanding the dynamics of Toll-like Receptor 5 response to flagellin and its regulation by estradiol.Scientific reports · 2017Article
- Signal transduction controls heterogeneous NF-κB dynamics and target gene expression through cytokine-specific refractory states.Nature communications · 2016Article
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- CellECT: cell evolution capturing tool.BMC bioinformatics · 2016Article
- Frequency Modulated Translocational Oscillations of Nrf2 Mediate the Antioxidant Response Element Cytoprotective Transcriptional Response.Antioxidants & redox signaling · 2015Article
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Abstract
An interactive method is proposed for complex cell segmentation, in particular of clustered cells. This article has two main contributions: First, we explore a hybrid combination of the random walk and the geodesic graph based methods for image segmentation and propose the novel concept of geodesic commute distance to classify pixels. The computation of geodesic commute distance requires an eigenvector decomposition of the weighted Laplacian matrix of a graph constructed from the image to be segmented. Second, by incorporating pairwise constraints from seeds into the algorithm, we present a novel method for eigenvector decomposition, namely a constrained density weighted Nyström method. Both visual and quantitative comparison with other semiautomatic algorithms including Voronoi-based segmentation, grow cut, graph cuts, random walk, and geodesic method are given to evaluate the performance of the proposed method, which is a powerful tool for quantitative analysis of clustered cell images in live cell imaging.
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