Cell Segmentation in Microscopy Imagery using a Bag of Local Bayesian Classifiers
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.
Recommended Citation
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 https://doi.org/10.1109/ISBI.2010.5490399
Meeting Name
2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 (2010: Apr. 14-17, Rotterdam, Netherlands)
Department(s)
Computer Science
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
International Standard Book Number (ISBN)
978-1424441266
International Standard Serial Number (ISSN)
1945-7928
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2010 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Apr 2010
