Quantification of retinal artery-vein ratio for vascular disease diagnosis using spatial U-Net

Autor:	Lakshmi Kala Pampana, Manjula Sri Rayudu
Rok vydání:	2022
Předmět:	Control and Optimization Life threat risk factors Computer Networks and Communications Hardware and Architecture Spatial segmentation Signal Processing Systemic disorders Vascular caliber Electrical and Electronic Engineering Classification Biomarkers Information Systems
Zdroj:	Indonesian Journal of Electrical Engineering and Computer Science. 27:1404
ISSN:	2502-4760 2502-4752
Popis:	The retinal vascular morphological caliber changes reveal the signs of systemic health disorder and life threat diseases such as cardio and cerebral diseases. The quantitative vascular parameters like narrowed arteries, widened venules, reduced artery-vein ratio (AVR) have been associated with aforesaid disorders and diseases. Hence the quantitative biomarker AVR is important parameter in diagnosing variety of diseases. The accurate quantification of AVR be possible if and only if accurate classification of arteries and vein is done. In this paper, we proposed a deep learning based robust vessel segmentation and classification algorithm based on spatial U-Net and the accuracy of the algorithm is 97.8%. In the quantification process, this algorithm is applied on region of interest (RoI) of a fundus image and measured the AVR values using central retinal artery equivalent (CRAE) and central retinal vein equivalent (CRVE). The experimentation is carried on the digital retinal images for vessel extraction (DRIVE) dataset. The outcome of this work is the AVR observed to be >0.4 in normal retinal case and AVR value
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fdf752d2a17197da0e3902260ec1c1c8 https://doi.org/10.11591/ijeecs.v27.i3.pp1404-1411 Zobrazit plný text záznamu