Shape-driven Segmentation of Intravascular Ultrasound Images

Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in study of plaque characteristics, mechanical properties of the arterial wall, its 3D reconstruction, and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition, we utilize a non-parametric probability density based image energy, with global image measurements rather than pointwise measurements used in previous methods. This greatly enhances our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach.