Glomeruli segmentation based on neural network with fault tolerance analysis

Jun Zhang, Jinglu Hu

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Image segmentation, which is the first essential and fundamental issue in the image analysis and pattern recognition, is a classical difficult problem in image processing. In the computer-aided diagnosis system of the renal biopsy images in microscope, the correct segmentation of glomerulus is an important step for automatic analysis. Complex characteristics of renal biopsy images lead to the difficulty in boundary features description. A kind of feature operator based on the definition of the cavum boundary is proposed in this paper. According to this operator, a nonlinear thresholding surface can be constructed by neural network, and the appropriate surface can be selected to enhance the cavum boundary by the fault tolerance analysis. After denoising, the segmentation results can be obtained. Experimental results indicate that this method can enhance the boundary and suppress noises at the same time; it can obtain good segmented results and has a fine adaptability to various sample images.

本文言語English
ホスト出版物のタイトルProceedings of the 2008 International Symposium on Computational Intelligence and Design, ISCID 2008
ページ401-404
ページ数4
DOI
出版ステータスPublished - 2008
イベント2008 International Symposium on Computational Intelligence and Design, ISCID 2008 - Wuhan, China
継続期間: 2008 10 172008 10 17

出版物シリーズ

名前Proceedings of the 2008 International Symposium on Computational Intelligence and Design, ISCID 2008
1

Conference

Conference2008 International Symposium on Computational Intelligence and Design, ISCID 2008
CountryChina
CityWuhan
Period08/10/1708/10/17

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture

フィンガープリント 「Glomeruli segmentation based on neural network with fault tolerance analysis」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