Original scientific paper
https://doi.org/10.1080/00051144.2023.2205727
Identification of coronary artery stenosis based on hybrid segmentation and feature fusion
K. Kavipriya
; Department of Computer Science, Christ Deemed to be University, Bangalore, India
*
Manjunatha Hiremath
; Department of Computer Science, Christ Deemed to be University, Bangalore, India
* Corresponding author.
Abstract
Coronary artery disease has been the utmost mutual heart disease in the past decades. Various research is going on to prevent this disease. Obstructive CAD occurs when one or more of the coronary arteries which supply blood to myocardium are narrowed owing to plaque build-up on the arteries’ inner walls, causing stenosis. The fundamental task required for the interpretation of coronary angiography is identification and quantification of severity of stenosis within the coronary circulation. Medical experts use X-ray coronary angiography to identify blood vessel/artery stenosis. Due to the artefact, the image has less clarity and it will be challenging for the medical expert to find the stenosis in the coronary artery. The solution to the problem a computational framework is proposed to segment the artery and spot the location of stenosis in the artery. Here the author presented an automatic method to detect stenosis from the X-ray angiogram image. A unified Computational method of Jerman, Level-set, fine-tuning the artery structure, is developed to extract the segmented artery features and detect the artery’s stenosis. The current experimental outcomes illustrate that this computational method achieves average specificity, sensitivity, Accuracy, precision and F-scores of 95%, 97.5%, 98%, 97.5% and 97.5%, respectively.
Keywords
Coronary artery disease; stenosis; X-ray angiography; Keyframe extraction; stenosis detection
Hrčak ID:
315889
URI
Publication date:
2.5.2023.
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