Automatic Fuzzy-based Hybrid Approach for Segmentation and Centerline Extraction of Main Coronary Arteries

Abstract

Coronary arteries segmentation and centerlines extraction is an important step in Coronary Artery Disease diagnosis. The main purpose of the fully automated presented approaches is helping the clinical non-invasive diagnosis process to be done in fast way with accurate result. In this paper, a hybrid scheme is proposed to segment the coronary arteries and to extract the centerlines from Computed Tomography Angiography volumes. The proposed automatic hybrid segmentation approach combines the Hough transform with a fuzzy-based region growing algorithm. First, a circular Hough transform is used to detect initially the aorta circle. Then, the well-known Fuzzy c-mean algorithm is employed to detect the seed points for the region growing algorithm resulting in 3D binary volume. Finally, the centerlines of the segmented arteries are extracted based on the segmented 3D binary volume using a skeletonization based method. Using a benchmark database provided by the Rotterdam Coronary Artery Algorithm Evaluation Framework, the proposed algorithm is tested and evaluated. A comparative study shows that the proposed hybrid scheme is able to achieve a higher accuracy, in comparison to the most related and recent published work, at reasonable computational cost.

FCM-Based Approach for Locating Visible Video Watermarks

“…It is clearly seen from the previous literature that the real-world limitations, such as noise, shadowing, and variations in cameras, are significant factors that make the problem of localizing watermarks more challenging. For decades, fuzzy logic and fuzzy-based techniques have been used successfully to deal with a wide variety of challenges in different research areas [25][26][27]. In addition, it is well known that the Fuzzy C-Means classifier is an efficient classifier that has been used in different research areas to improve the accuracies of clustering problems [27,28].…”