Cell Segmentation in Microscopy Imagery Using a Bag of Local Bayesian Classifiers

Zhaozheng Yin, Ryoma Bise, Mei Chen, and Takeo Kanade
The IEEE International Symposium on Biomedical Imaging (ISBI) 2010, April, 2010.


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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",
}