Automatic detection of dense calcium and acoustic shadow in intravascular ultrasound images by dual-threshold-based segmentation approach

The purpose of this study was to automatically detect dense calcium (DC) and acoustic shadow regions in intravascular ultrasound (IVUS) images by a dual-threshold-based segmentation approach. Three hundred grayscale IVUS and corresponding virtual histology (VH)-IVUS images of human coronary arteries were obtained using a 20 MHz commercial catheter. Plaque regions between intima and media-adventitial borders were manually extracted from all IVUS images. To detect DC and acoustic shadow regions automatically, DC candidates were first selected from plaque regions on the basis of intensity. The shadow mask of each DC candidate was then obtained by calculating its centroid. A DC candidate involving acoustic shadow was finally selected as DC tissue. The segmentation performance of the proposed approach was quantitatively evaluated using the area difference, DC ratio, Hausdorff distance, and Dice similarity coefficient. Quantitative results indicated that all the parameters for the proposed approach were highly similar to those of VH-IVUS. Despite the relatively low agreement (64.1%) for the DC tissue, reliable performance was found for the proposed approach. These experimental results suggest that the proposed method has clinical applicability for diagnosing cardiovascular diseases in IVUS images.