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A Review of Coronary Vessel Segmentation Algorithms Publisher



Dehkordi MT1, 2 ; Sadri S1, 2 ; Doosthoseini A1
Authors
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Authors Affiliations
  1. 1. Department of Electrical and Computer Engineering, Digital Signal Processing Laboratory, Isfahan University of technology, Isfahan, Iran
  2. 2. Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

Source: Journal of Medical Signals and Sensors Published:2011


Abstract

Coronary heart disease has been one of the main threats to human health. Coronary angiography is taken as the gold standard; for the assessment of coronary artery disease. However, sometimes, the images are difficult to visually interpret because of the crossing and overlapping of vessels in the angiogram. Vessel extraction from X-ray angiograms has been a challenging problem for several years. There are several problems in the extraction of vessels, including: weak contrast between the coronary arteries and the background, unknown and easily deformable shape of the vessel tree, and strong overlapping shadows of the bones. In this article we investigate the coronary vessel extraction and enhancement techniques, and present capabilities of the most important algorithms concerning coronary vessel segmentation.
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2. An Improved Seed Point Detection Algorithm for Centerline Tracing in Coronary Angiograms, ITNG2010 - 7th International Conference on Information Technology: New Generations (2010)
5. Extraction of Retinal Blood Vessels by Curvelet Transform, Proceedings - International Conference on Image Processing, ICIP (2009)
6. Vessel Centerlines Extraction From Fundus Fluorescein Angiogram Based on Hessian Analysis of Directional Curvelet Subbands, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (2013)
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