Title

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.

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)

9781424441266

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.

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