Semi-automatic matching of OCT and IVUS images for image fusion

Olivier Pauly, Gozde Unal*, Greg Slabaugh, Stephane Carlier, Tong Fang

*Corresponding author for this work

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

7 Citations (Scopus)


Medical imaging is essential in the diagnosis of atherosclerosis. In this paper, we propose the semi-automatic matching of two promising and complementary intravascular imaging techniques, Intravascular Ultrasound (IVUS) and Optical Coherence Tomography (OCT), with the ultimate goal of producing hybrid images with increased diagnostic value for assessing arterial health. If no ECG gating has been performed on the IVUS and OCT pullbacks, there is typically an anatomical shuffle (displacement in time and space) in the image sequences due to the catheter motion in the artery during the cardiac cycle, and thus, this is not possible to perform a 3D registration. Therefore, the goal of our work is to detect semi-automatically the corresponding images in both modalities as a preprocessing step for the fusion. Our method is based on the characterization of the lumen shape by a set of Gabor Jets features. We also introduce different correction terms based on the approximate position of the slice in the artery. Then we train different support vector machines based on these features to recognize these correspondences. Experimental results demonstrate the usefulness of our approach, which achieves up to 95% matching accuracy for our data.

Original languageEnglish
Title of host publicationMedical Imaging 2008
Subtitle of host publicationImage Processing
Publication statusPublished - 2008
Externally publishedYes
EventMedical Imaging 2008: Image Processing - San Diego, CA, United States
Duration: 17 Feb 200819 Feb 2008

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2008: Image Processing
Country/TerritoryUnited States
CitySan Diego, CA


  • Atherosclerosis
  • Feature extraction
  • Intravascular ultrasound
  • Machine learning
  • Matching
  • Optical coherence tomography
  • Pattern recognition
  • Registration


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