Automatic segmentation of artery wall in coronary IVUS images: a probabilistic approach

摘要

Intravascular ultrasound images represent a unique tool to analyze the morphology of arteries and vessels (plaques, restenosis, etc.). The poor quality of these images makes unsupervised segmentation based on traditional segmentation algorithms (such as edge or ridge/valley detection) fail to achieve the expected results. Here, the authors present a probabilistic flexible template to separate different regions in the image. In particular, they use elliptic templates to model and detect the shape of the vessel inner wall in IVUS images. They present the results of successful segmentation obtained from patients undergoing stent treatment. A physician team has validated these results.