Segmentation of intravascular ultrasound images based on convex–concave adjustment in extreme regions

The extraction of intima and adventitia from intravascular ultrasound (IVUS) images is of great significance for the diagnosis and treatment of coronary artery disease. However, traditional IVUS image segmentation methods have complex modeling, poor robustness, and need to design different algorithms for internal and external boundaries. This article proposes a method of simultaneous intima and adventitia. We firstly detect extreme regions of the pre-processed image, and then design a screening vector to extract two extreme regions representing the lumen and media. After that, the convex–concave boundaries of the two regions are adjusted by the opening operation with a variable radius of the structural element and the superposition of a circle. Finally, the contours are fitted with ellipse to complete the segmentation. In order to evaluate the performance of the method, we first qualitatively display the extreme region contours and the final contours, and compare the results with those drawn manually by clinical experts. Then, we use Jaccard measure, Dice coefficient, percentage of area difference and Hausdorff distance to test the robust performance and generalization performance, and the index values of inner and outer borders are 0.92 ± 0.03, 0.96 ± 0.02, 0.06 ± 0.04, 0.26 ± 0.07 mm, and 0.92 ± 0.04, 0.95 ± 0.02, 0.06 ± 0.05, 0.29 ± 0.08 mm, respectively. Besides, we make a quantitative comparison with the relevant studies. Experiment results show that the proposed automatic methods not only have high accuracy, but also have good robustness.