Segmentation d’images intravasculaires ultrasonores
Intravascular ultrasound (IVUS) is a catheter based medical imaging technique that produces cross-sectional images of blood vessels. These images provide quantitative assessment of the vascular wall, information about the nature of atherosclerotic lesions as well as the plaque shape and size. Over the past few years, this medical imaging modality has become a useful tool in research and clinical applications, particularly in atherosclerotic disease studies.   However, IVUS imaging is subject to catheter ring-down artifacts, missing vessel parts due to calcification shadowing or side-branches, heterogeneously looking plaques and ultrasonic speckle from blood. The automated analysis of large IVUS data sets thus represents an important challenge.  A three-dimensional segmentation algorithm based on the multiple interface fast-marching method is presented. The segmentation is based on region and contour features of the IVUS images: a new speed fonction for the interface propagation that combines the probability density functions (PDFs) of the vessel wall components and the intensity gradients is proposed. The segmentation is highly automated with the detection of the lumen boundary that is fully automatic. Minimal interactions are necessary with a novel initialization procedure since initial contours of the external vessel wall border are also computed automatically on a limited number of longitudinal images and then proposed to the user for acceptance or correction.   The segmentation method was validated with in-vivo IVUS data sets acquired from femoral arteries. This database contained 3 subgroups: pullbacks acquired before balloon angioplasty, after the intervention and at a 1 year follow-up examination. Results were compared with validation contours that were manually traced by different experts in IVUS image analysis. The lumen and external wall boundaries detected with the fast-marching method are in agreement with the experts' manually traced contours with similarly found area measurements and small point-to-point contour differences. In addition, the 3D fast-marching segmentation method dramatically reduced the analysis time compared to manual tracing. Such a valdiation study, with comparison between pre- and post-intervention data, has never been reported in the IVUS segmentation literature.  In conclusion, the fast-marching method combining the information on the gray level distributions and intensity gradients of the images is precise and efficient to analyze large IVUS sequences. It is hoped that the fast-marching method will become a widely used tool for the fastidious and difficult task of IVUS image processing.
segmentation level-sets fast-marching Intravascular ultrasound imaging segmentation level-sets fast-marching gray level probabilty density functions gray level gradients