Interactive blood vessel segmentation from retinal fundus image based on canny edge detector

Ibrahim, Haidi and Ooi, Alexander Ze Hwan and Soo, Siang Teoh and Embong, Zunaina and Abd Hamid, Aini Ismafairus and Zainon, Rafidah and Shir, Li Wang and Theam, Foo Ng and Hamzah, Rostam Affendi (2021) Interactive blood vessel segmentation from retinal fundus image based on canny edge detector. Sensors, 21 (19). pp. 1-22. ISSN 1424-8220

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Abstract

Optometrists, ophthalmologists, orthoptists, and other trained medical professionals use fundus photography to monitor the progression of certain eye conditions or diseases. Segmentation of the vessel tree is an essential process of retinal analysis. In this paper, an interactive blood vessel segmentation from retinal fundus image based on Canny edge detection is proposed. Semi automated segmentation of specific vessels can be done by simply moving the cursor across a particular vessel. The pre-processing stage includes the green color channel extraction, applying Contrast Limited Adaptive Histogram Equalization (CLAHE), and retinal outline removal. After that, the edge detection techniques, which are based on the Canny algorithm, will be applied. The vessels will be selected interactively on the developed graphical user interface (GUI). The program will draw out the vessel edges. After that, those vessel edges will be segmented to bring focus on its details or detect the abnormal vessel. This proposed approach is useful because different edge detection parameter settings can be applied to the same image to highlight particular vessels for analysis or presentation.

Item Type:	Article
Uncontrolled Keywords:	Blood vessels, Edge segmentation, Fundus images, Retinal
Divisions:	Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Sabariah Ismail
Date Deposited:	30 May 2022 12:00
Last Modified:	17 Jul 2023 12:04
URI:	http://eprints.utem.edu.my/id/eprint/25969
Statistic Details:	View Download Statistic

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