Cell Segmentation in Microscopy Imagery using a Bag of Local Bayesian Classifiers
Cell segmentation in microscopy imagery is essential for many bioimage applications such as cell tracking. To segment cells from the background accurately, we present a pixel classification approach that is independent of cell type or imaging modality. We train a set of Bayesian classifiers from clustered local training image patches. Each Bayesian classifier is an expert to make decision in its specific domain. The decision from the mixture of experts determines how likely a new pixel is a cell pixel. We demonstrate the effectiveness of this approach on four cell types with diverse morphologies under different microscopy imaging modalities.
Z. Yin et al., "Cell Segmentation in Microscopy Imagery using a Bag of Local Bayesian Classifiers," Proceedings of the 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 (2010, Rotterdam, Netherlands), pp. 125-128, Institute of Electrical and Electronics Engineers (IEEE), Apr 2010.
The definitive version is available at http://dx.doi.org/10.1109/ISBI.2010.5490399
2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 (2010: Apr. 14-17, Rotterdam, Netherlands)
Keywords and Phrases
Bayesian Classifier; Cell Segmentation; Cell Tracking; Cell Types; Imaging Modality; Local Training; Microscopy Images; Microscopy Imaging; Mixture of Experts; Pixel Classification; Bayesian Networks; Classifiers; Image Segmentation; Medical Imaging; Mixtures; Pixels; Cells; Microscopy Image
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Article - Conference proceedings
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