Determining the number of clusters for nuclei segmentation in breast cancer image

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

Abstract

Clustering is commonly technique for image segmentation, however determining an appropriate number of clusters is still challenging. Due to nuclei variation of size and shape in breast cancer image, an automatic determining number of clusters for segmenting the nuclei breast cancer is proposed. The phase of nuclei segmentation in breast cancer image are nuclei detection, touched nuclei detection, and touched nuclei separation. We use the Gram-Schmidt for nuclei cell detection, the geometry feature for touched nuclei detection, and combining of watershed and spatial k-Means clustering for separating the touched nuclei in breast cancer image. The spatial k-Means clustering is employed for separating the touched nuclei, however automatically determine the number of clusters is difficult due to the variation of size and shape of single cell breast cancer. To overcome this problem, first we apply watershed algorithm to separate the touched nuclei and then we calculate the distance among centroids in order to solve the over-segmentation. We merge two centroids that have the distance below threshold. And the new of number centroid as input to segment the nuclei cell using spatial k- Means algorithm. Experiment show that, the proposed scheme can improve the accuracy of nuclei cell counting.

Original languageEnglish
Title of host publicationEighth International Conference on Graphic and Image Processing, ICGIP 2016
EditorsZhu Zeng, Tuan D. Pham, Vit Vozenilek
PublisherSPIE
ISBN (Electronic)9781510609518
DOIs
Publication statusPublished - 2017
Event2016 8th International Conference on Graphic and Image Processing, ICGIP 2016 - Tokyo, Japan
Duration: 29 Oct 201631 Oct 2016

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10225
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference2016 8th International Conference on Graphic and Image Processing, ICGIP 2016
Country/TerritoryJapan
CityTokyo
Period29/10/1631/10/16

Keywords

  • Clustering
  • Gram-Schmidt
  • breast cancer image
  • nuclei segmentation
  • spatial k-Means
  • watershed

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