Automatical vessel wall detection in intravascular coronary OCT

Kai Pin Tung, Wen Zhe Shi, Ranil De Silva, Eddie Edwards, Daniel Rueckert

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

32 Scopus citations

Abstract

The aim of this study is to automatically detect the boundary of vessel walls in optical coherence tomography (OCT) sequences. We developed a new method to eliminate guide-wire shadow artifacts and accurately estimate the vessel wall. The estimation of the position of the guide-wire is the key concept for the elimination of guide-wire shadow artifacts. After identification of the artifacts we propose a geometrically-based method which can be applied to OCT cross-section images to remove the artifacts. The segmentation approach is based on a novel combination of expectation maximization (EM) based segmentation and graph cut (GC) based segmentation. Validation is performed using simulated data and 4 typical in vivo OCT sequences. The comparison against manual expert segmentation demonstrates that the proposed vessel wall identification is robust and accurate.

Original languageEnglish
Title of host publication2011 8th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI'11
Pages610-613
Number of pages4
DOIs
StatePublished - 2011
Externally publishedYes
Event2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 - Chicago, IL, United States
Duration: 30 Mar 20112 Apr 2011

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
Country/TerritoryUnited States
CityChicago, IL
Period30/03/112/04/11

Keywords

  • Active Contour
  • Convex Hull
  • Expectation Maximization
  • Graph Cuts
  • OCT
  • Vessel Wall Detection

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