A probabilistic segmentation method for the identification of luminal borders in intravascular ultrasound images

Intravascular ultrasound (IVUS) is a catheter-based medical imaging technique that produces cross-sectional images of blood vessels and is particularly useful for studying atherosclerosis. In this paper, we present a probabilistic approach for the semi-automatic identification of the luminal border on IVUS images. Specifically, we parameterize the lumen contour using a mixture of Gaussian that is deformed by the minimization of a cost function formulated using a probabilistic approach. For the optimization of the cost function, we introduce a novel method that linearly combines the descent directions of the steepest descent and BFGS optimization methods within a trust region that improves convergence. Results of our proposed method on 20 MHz IVUS images are presented and discussed in order to demonstrate the effectiveness of our approach.