An automated model for rapid and reliable segmentation of intravascular ultrasound images

  • Authors:

  • Eirini Parissi;Yiannis Kompatsiaris;Yiannis S. Chatzizisis;Vassilis Koutkias;Nicos Maglaveras;M. G. Strintzis;George D. Giannoglou

  • Affiliations:

  • Centre for Research and Technology-Hellas, Informatics and Telematics Institute, Thessaloniki, Greece;Centre for Research and Technology-Hellas, Informatics and Telematics Institute, Thessaloniki, Greece;Cardiovascular Engineering and Atherosclerosis Laboratory, 1st Cardiology Department, AHEPA University Hospital, Faculty of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece;Laboratory of Medical Informatics, Faculty of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece;Laboratory of Medical Informatics, Faculty of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece;Centre for Research and Technology-Hellas, Informatics and Telematics Institute, Thessaloniki, Greece;Cardiovascular Engineering and Atherosclerosis Laboratory, 1st Cardiology Department, AHEPA University Hospital, Faculty of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece

  • Venue:
  • ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
  • Year:
  • 2006

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Abstract

The detection of lumen and media-adventitia borders in intravascular ultrasound (IVUS) images constitutes a necessary step for accurate morphometric analyses of coronary plaques and accordingly assessment of the atherosclerotic lesion length. Aiming to tackle this issue, an automated model for lumen and media-adventitia border detection is presented, which is based on active contour models. The proposed approach enables extraction of the corresponding boundaries in sequential IVUS frames by applying an iterative procedure, in which initialization of the two contours in each frame is performed automatically, based on the segmentation of its previous frame. The above procedure is implemented through a user-friendly interface, permitting the interaction of the user when needed. The in vivo application and evaluation of our model in sequential IVUS images indicated that the proposed approach is capable of accurately and rapidly segmenting hundreds of IVUS images.