An automatic segmentation of calcified tissue in forward-looking intravascular ultrasound images

Abstract

The assessment of images of the coronary artery system plays a crucial part in the diagnosis and treatment of cardiovascular diseases (CVD). Forward-looking intravascular ultrasound (FL-IVUS) has a distinct advantage in assessing CVD due to its superior resolution and imaging capability, especially in severe calcification scenarios. The demarcation of the lumen and media-adventitia, as well as the identification of calcified tissue information, constitute the initial steps in assessing of CVD such as atherosclerosis using FL-IVUS images. In this research, we introduced a novel approach for automated lumen segmentation and identification of calcified tissue in FL-IVUS images. The proposed method utilizes superpixel segmentation and fuzzy C-means clustering (FCM) to identify regions that potentially correspond to lumina. Furthermore, connected component labeling and active contour methods are employed to refine the contours of lumina. To handle the distinctive depth information found in FL-IVUS images, ellipse fitting and region detectors are applied to identify areas with calcified tissue. In our dataset consisting of 43 FL-IVUS images, this method achieved mean values for Jaccard measure, Dice coefficient, Hausdorff distance, and percentage area difference at 0.952 ± 0.016, 0.975 ± 0.008, 0.296 ± 0.186, and 0.019 ± 0.010, respectively. Furthermore, when compared with traditional segmentation approaches, the proposed approach yields higher images quality. The test results demonstrate the effectiveness of this innovative automated segmentation technique for detecting the lumina and calcified tissue in FL-IVUS images.