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

Zhaozheng Yin, Ryoma Bise, Mei Chen, Takeo Kanade

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

53 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトル2010 7th IEEE International Symposium on Biomedical Imaging
ホスト出版物のサブタイトルFrom Nano to Macro, ISBI 2010 - Proceedings
出版社IEEE Computer Society
ページ125-128
ページ数4
ISBN(印刷版)9781424441266
DOI
出版ステータス出版済み - 2010
外部発表はい
イベント7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Rotterdam, オランダ
継続期間: 4 14 20104 17 2010

出版物シリーズ

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

その他

その他7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010
国/地域オランダ
CityRotterdam
Period4/14/104/17/10

All Science Journal Classification (ASJC) codes

  • 生体医工学
  • 放射線学、核医学およびイメージング

フィンガープリント

「Cell segmentation in microscopy imagery using a bag of local Bayesian classifiers」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル