A novel adaptive local thresholding approach for segmentation of HEp-2 cell images

Xiande Zhou, Yuexiang Li, Linlin Shen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

The patterns of Human Epithelial type 2 (HEp-2) cell provide useful information for the diagnosis of systemic autoimmune diseases. However, the recognition of cell patterns requires manual annotation by experienced physicians, which is subject to inter-observer variability. Therefore, an automatic diagnosis system is desirable. As the crucial pre-processing step for cell pattern recognition, the performance of cell segmentation is crucial. In this paper, a novel adaptive local thresholding approach is proposed to solve the issue. The approach divides cell images into overlapping sub-images and applies adaptive threshold estimator to each of them. The ICPR 2014 HEp-2 cell datasets are employed to assess the segmentation performance of our framework. The results show that the system achieves an average segmentation accuracy of 66.95%, which outperforms the typical thresholding approaches.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages174-178
Number of pages5
ISBN (Electronic)9781509023769
DOIs
Publication statusPublished - 27 Mar 2017
Externally publishedYes
Event2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016 - Beijing, China
Duration: 13 Aug 201615 Aug 2016

Publication series

Name2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016

Conference

Conference2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016
Country/TerritoryChina
CityBeijing
Period13/08/1615/08/16

Keywords

  • Adaptive thresholding
  • Cell images
  • Local thresholding
  • THRESHOLD estimator

ASJC Scopus subject areas

  • Signal Processing

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