Automatic 3d segmentation of intravascular ultrasound images using region and contour information

Authors:
Marie-Hélène Roy Cardinal;Jean Meunier;Gilles Soulez;Roch L. Maurice;Éric Thérasse;Guy Cloutier
Affiliations:
Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital, Canada;Département d’Informatique et de Recherche Opérationnelle, University of Montreal, Canada;Radiology Department, University of Montreal Hospital, Canada;Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital, Canada;Radiology Department, University of Montreal Hospital, Canada;Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital, Canada
Venue:
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2005

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Abstract

Intravascular ultrasound (IVUS) produces images of arteries that show the lumen in addition to the layered structure of the vessel wall. A new automatic 3D IVUS fast-marching segmentation model is presented. The method is based on a combination of region and contour information, namely the gray level probability density functions (PDFs) of the vessel structures and the image gradient. Accurate results were obtained on in-vivo and simulated data with average point to point distances between detected vessel wall boundaries and validation contours below 0.105 mm. Moreover, Hausdorff distances (that represent the worst point to point variations) resulted in values below 0.344 mm, which demonstrate the potential of combining region and contour information in a fast-marching scheme for 3D automatic IVUS image processing.