Space Curve Approach for IVUS Image Segmentation

Intravascular ultrasound imaging (IVUS) is an interventional cardiology technique for assessing atherosclerosis lesions in artery. This technique generates images showing the different layers of the artery and allows quantitative measurements reflecting its condition. However due to the acquisition process these images are affected by artifacts like ring-down, guide wire and shadows generated by tissue calcification. In this paper we develop a 3D algorithm based on a helical snake (active contour) for the lumen segmentation in intravascular ultrasound images. The helix snake evolves based on the analysis of the statistical properties computed on windows inside and outside the contour until it reaches the luminal border. In addition we show the influence of the ring-down artifact for the luminal border detection by adding a pre-processing step for reducing its adverse effect. The algorithm was executed on 2190 images from two clinical IVUS sequences of femoral arteries presenting the ringdown artifact. The performance of the algorithm was evaluated with respect to expert manual plots and gave a mean Hausdorff distance of 0.31 mm with overlap of 89.50 % and 94.38 % for respectively Jaccard and Dice indexes improving the result by 0.29 mm, 8.79 % and 5.36 % compared to the result without artifact removal.