Automated detection of wall and plaque borders in intravascular ultrasound images

Abstract

Intravascular ultrasound is a minimally invasive tomographic technique which produces 2-D cross-sectional images depicting vessel wall architecture and plaque morphology. Currently, no reliable automated approaches exist that offer segmentation of blood and vascular wall. We have developed a method for automated segmentation of intravascular ultrasound images to differentiate among plaque, wall, and blood. To achieve reliable border detection in noisy intravascular ultrasound images, a priori knowledge is incorporated in the edge detection process using heuristic graph searching. The method was validated using images from two phantoms that were imaged under several pressure conditions. In the first image set, our automated border detection method correctly identified the wall and plaque borders in 69/91 images. In the second image set, our method successfully identified external and internal wall and plaque borders in all 36 images. Lumen cross-sectional areas correlated very well with distending pressure in both sets of images. By comparison with the micrometer determined average wall thickness, mean absolute error of wall thickness was 0.02 +/- 0.01 mm.