Region segmentation in 3-D optical coherence tomography images

Cheng Wei Chou, Jiann Der Lee*, Carol T. Liu, Meng Tsan Tsai

*Corresponding author for this work

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

1 Scopus citations

Abstract

This paper describes a novel region segmentation method created to enhance spatial relationships in 3-D optical coherence tomography (OCT) images. To reduce the noise and distortion problems in low-resolution OCT images, previous work used the mean value and an enhanced-fuzzy-c-mean algorithm to cluster pixels in 2-D OCT images and find the edge between different clustered regions. To utilize more spatial relationships and to reduce computation time, the proposed method uses the mean value and a 3-D filter-based-fuzzy-c-mean algorithm to cluster pixels in 3-D OCT images and find the edge between different clustered regions. The OCT images of an artificial object used to simulate vessels are tested in the experiment, and the segmented regions of interest are reconstructed via AVIZO for 3-D display purposes.

Original languageEnglish
Title of host publication2014 IEEE International Symposium on Bioelectronics and Bioinformatics, IEEE ISBB 2014
PublisherIEEE Computer Society
ISBN (Print)9781479927708
DOIs
StatePublished - 2014
Event2014 IEEE International Symposium on Bioelectronics and Bioinformatics, IEEE ISBB 2014 - Chung Li, Taiwan
Duration: 11 04 201414 04 2014

Publication series

Name2014 IEEE International Symposium on Bioelectronics and Bioinformatics, IEEE ISBB 2014

Conference

Conference2014 IEEE International Symposium on Bioelectronics and Bioinformatics, IEEE ISBB 2014
Country/TerritoryTaiwan
CityChung Li
Period11/04/1414/04/14

Keywords

  • 3-D segmentation
  • OCT
  • Optical coherence tomography
  • fuzzy-c-mean
  • vessel

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