Tortuosity of retinal blood vessels has been identified as one of earliest indicators to a number of vascular and nonvascular diseases; therefore, early detection and grading blood vessel tortuosity could help for early diseases prevention of further complications. There have been many attempts to develop an accurate automated tortuosity grading measure or system. These attempts have varied, from classifying vessels as either tortuous or non-tortuous or classifying/grading a number of retinal vessels in increased tortuosity, to evaluate the collective tortuosity of a whole vascular tree. Yet, seem none of these systems has gained a universal acceptance. This paper provides an overview of systems and measures, either automatic or manual, which are most proposed, to quantify tortuosity, and to critically evaluate the strength and limitations of those systems and measures; also it shed light on problems encountered by researchers in this field, such as the absence of unified, publicly available datasets and the limitations of the existing ones in terms of datasets sizes, variety based on pathologies and the suitability of vessels segments used in these datasets.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.