Cell segmentation in microscopy imagery using a bag of local Bayesian classifiers

Zhaozheng Yin, Ryoma Bise, Mei Chen, Takeo Kanade

Research output: Chapter in Book/Report/Conference proceedingConference contribution

53 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2010 7th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2010 - Proceedings
PublisherIEEE Computer Society
Pages125-128
Number of pages4
ISBN (Print)9781424441266
DOIs
Publication statusPublished - 2010
Event7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Rotterdam, Netherlands
Duration: Apr 14 2010Apr 17 2010

Publication series

Name2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings

Other

Other7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010
CountryNetherlands
CityRotterdam
Period4/14/104/17/10

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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