An Efficient Coronary Disease Diagnosis System Using Dual-Phase Multi-Objective Optimization and Embedded Feature Selection
Developments in healthcare technologies have significantly enhanced spatial resolution and improved contrast resolution, permitting analysis of additional subtle structures than formerly attainable. An approach for Automatic recognition and quantification of calcifications from arteries in computed tomography (CT) scans is developed which is a key necessity in planning the treatment of individuals with suspected coronary artery disease. First, a Dual-Phase Multi-_objective Optimization approach using an Active Contour Model-based region-growing technique is developed. Second, an embedded feature selection method is developed with an expert classifier to detect calcified objects in the segmented artery with great accuracy. Finally, the Agatston scoring method is utilized to quantify the level of coronary artery calcium plaque. Coronary CT images from the AS+CT scanner with a slice thickness of 3 mm were obtained from clinical practice. Experimental results demonstrate that our proposed method improves the accuracy of lesion detection for better treatment planning.
Year of publication: |
2017
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Authors: | Priyatharshini R. ; Chitrakala S. |
Published in: |
International Journal of Intelligent Information Technologies (IJIIT). - IGI Global, ISSN 1548-3665, ZDB-ID 2400990-8. - Vol. 13.2017, 3 (01.07.), p. 15-36
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Publisher: |
IGI Global |
Subject: | Calcium Object Detection | Coronary Artery Segmentation | Coronary Disease Diagnosis | Embedded Feature Selection | Multi-Objective Optimization |
Saved in:
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