Paper Info
Title
Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector
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.
Year
DOI
Venue
2021
10.3390/s21196380
SENSORS
Keywords
DocType
Volume
blood vessels, edge segmentation, fundus images, retinal
Journal
21
Issue
ISSN
Citations 
19
1424-8220
1
PageRank 
References 
Authors
0.35
0
9
Name
Order
Citations
PageRank
Alexander Ze Hwan Ooi110.35
Zunaina Embong210.35
Aini Ismafairus Abd Hamid310.69
Rafidah Zainon410.35
Shir Li Wang532.45
Theam Foo Ng6414.86
Rostam Affendi Hamzah7232.58
Soo Siang Teoh810.35
Haidi Ibrahim91158.83