The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

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.

Notes

Associated Center(s) / Consortia: Medical Robotics Technology Center

Associated Lab(s) / Group(s): Tissue Engineering

Associated Project(s): Cell Tracking

Number of pages: 4

Text Reference

Zhaozheng Yin, Ryoma Bise, Mei Chen, and Takeo Kanade, "Cell Segmentation in Microscopy Imagery Using a Bag of Local Bayesian Classifiers," The IEEE International Symposium on Biomedical Imaging (ISBI) 2010, April, 2010.

BibTeX Reference

@inproceedings{Yin_2010_6588,
   author = "Zhaozheng Yin and Ryoma Bise and Mei Chen and Takeo Kanade",
   title = "Cell Segmentation in Microscopy Imagery Using a Bag of Local Bayesian Classifiers",
   booktitle = "The IEEE International Symposium on Biomedical Imaging (ISBI) 2010",
   month = "April",
   year = "2010",
}

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