Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Voronoi-Based segmentation of cells on image manifolds
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Cell segmentation in microscopy imagery using a bag of local Bayesian classifiers
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
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Studies of differentiation abilities of stem cells have been attracting a lot of attention over the last years. Microscopy can be used to record details of the differentiation process of stem cells under different perturbations and is an important tool for studying stem cell differentiation. Since it is infeasible to quantitatively analyze a huge amount of image data manually, automated image analysis systems are urgently needed. However, the complicated morphological appearances of stem cells are challenging to the existing segmentation methods. Herein, we propose a new, automated scheme for stem cell segmentation. This scheme first uses the multi-scale blob and curvilinear structure detectors to delineate the skeletons of stem cells quickly and then segment out stem cells by refining the skeletons to the cell boundaries using multi-level sets. The initial experimental results indicate the effectiveness of the proposed scheme.