Automatic IVUS lumen segmentation using a 3D adaptive helix model.

In this paper, we develop a three dimensional (3D) segmentation algorithm of the lumen visualized using intravascular ultrasound (IVUS) imaging. These images are known for their various granular textures (speckles) that make the discrimination of different tissues very difficult, especially as a result of the presence of artifacts and shadows generated by tissue calcification. Our model consists of a helical active contour initialized automatically over the sequence, that evolves based on the analysis of the Rayleigh distribution of gray levels in order to extract the luminal border. This novel algorithm is fast, uses an adaptive simple space curve for 3D extraction of the lumen, and is fully automatic. Consequently, it does not require an initialization close to the lumen border. Segmentation was carried out on 19 IVUS sequences with a total of 8918 images acquired in vivo on nine femoral and ten coronary arteries using a 20 MHz probe. These sequences showed many difficulties, such as severe stenosis, bifurcations, side vessels, shadows, and other artifacts. The quantitative evaluation of our algorithm compared to the ground truth for the femoral and coronary datasets showed an overlap greater than 89% for the Jaccard index and greater than 94% for the Dice index, yielding an accuracy of more than 98.5%. Several other metrics are also presented that confirm the efficiency of our helix model compared to other recent methods reported in the literature using a similar ultrasound probe.