Nuclei detection based on secant normal voting with skipping ranges in stained histopathological images

Research output: Contribution to journalArticle

Abstract

Seed detection or sometimes known as nuclei detection is a prerequisite step of nuclei segmentation which plays a critical role in quantitative cell analysis. The detection result is considered as accurate if each detected seed lies only in one nucleus and is close to the nucleus center. In previous works, voting methods are employed to detect nucleus center by extracting the nucleus saliency features. However, these methods still encounter the risk of false seeding, especially for the heterogeneous intensity images. To overcome the drawbacks of previous works, a novel detection method is proposed, which is called secant normal voting. Secant normal voting achieves good performance with the proposed skipping range. Skipping range avoids over-segmentation by preventing false seeding on the occlusion regions. Nucleus centers are obtained by mean-shift clustering from clouds of voting points. In the experiments, we show that our proposed method outperforms the comparison methods by achieving high detection accuracy without sacrificing the computational efficiency.

Original languageEnglish
Pages (from-to)523-530
Number of pages8
JournalIEICE Transactions on Information and Systems
VolumeE101D
Issue number2
DOIs
Publication statusPublished - 2018 Feb 1

Keywords

  • Hematoxylin and Eosin (H&E) staining
  • Nuclei detection
  • Secant normal voting
  • Skipping range
  • Splitting point

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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