Framework for detection and localization of coronary non-calcified plaques in cardiac CTA using mean radial profiles. (1st October 2017)

• Record Type: Journal Article
• Authors: Jawaid, Muhammad Moazzam; Riaz, Atif; Rajani, Ronak; Reyes-Aldasoro, Constantino Carlos; Slabaugh, Greg
• Is Part Of: Computers in biology and medicine. Volume 89(2017)
• Page Start: 84
• Page End: 95
• Publication Date: 2017-10-01
• Subjects: Coronary segmentation -- Non-calcified plaques -- Support vector machines -- Plaque localization; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2017.07.021
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background and objective: The high mortality rate associated with coronary heart disease (CHD) has driven intensive research in cardiac imaging and image analysis. The advent of computed tomography angiography (CTA) has turned non-invasive diagnosis of cardiovascular anomalies into reality as calcified coronary plaques can be easily identified due to their high intensity values. However, the detection of non-calcified plaques in CTA is still a challenging problem because of lower intensity values, which are often similar to the nearby blood and muscle tissues. In this work, we propose the use of mean radial profiles for the detection of non-calcified plaques in CTA imagery. Methods: Accordingly, we computed radial profiles by averaging the image intensity in concentric rings around the vessel centreline in a first stage. In the subsequent stage, an SVM classifier is applied to identify the abnormal coronary segments. For occluded segments, we further propose a derivative-based method to localize the position and length of the plaque inside the segment. Results: A total of 32 CTA volumes were analysed and a detection accuracy of 88.4% with respect to the manual expert was achieved. The plaque localization accuracy was computed using the Dice similarity coefficient and a mean of 83.2% was achieved. Conclusion: The consistent performance for multi-vendor, multi-institution data demonstrates the reproducibility of our method across different CTA datasets with a good agreement with manual expert annotations. Highlights: Coronary Tree Segmentation in Cardiac CTA. Geometrical evaluation using Arterial Remodelling. Discrete Radial Profile Based Intensity Analysis in CTA imagery. Non-Calcified Coronary Plaque Detection using Support Vector Machines. Second Order Derivative Based Plaque localization in Segmented Coronary Tree.