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The Edge Detectors Suitable for Retinal OCT Image Segmentation
Joint Authors
Luo, Su
Yang, Jing
Gao, Qian
Zhou, Sheng
Zhan, Chang'an A.
Source
Journal of Healthcare Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-08-17
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Retinal layer thickness measurement offers important information for reliable diagnosis of retinal diseases and for the evaluation of disease development and medical treatment responses.
This task critically depends on the accurate edge detection of the retinal layers in OCT images.
Here, we intended to search for the most suitable edge detectors for the retinal OCT image segmentation task.
The three most promising edge detection algorithms were identified in the related literature: Canny edge detector, the two-pass method, and the EdgeFlow technique.
The quantitative evaluation results show that the two-pass method outperforms consistently the Canny detector and the EdgeFlow technique in delineating the retinal layer boundaries in the OCT images.
In addition, the mean localization deviation metrics show that the two-pass method caused the smallest edge shifting problem.
These findings suggest that the two-pass method is the best among the three algorithms for detecting retinal layer boundaries.
The overall better performance of Canny and two-pass methods over EdgeFlow technique implies that the OCT images contain more intensity gradient information than texture changes along the retinal layer boundaries.
The results will guide our future efforts in the quantitative analysis of retinal OCT images for the effective use of OCT technologies in the field of ophthalmology.
American Psychological Association (APA)
Luo, Su& Yang, Jing& Gao, Qian& Zhou, Sheng& Zhan, Chang'an A.. 2017. The Edge Detectors Suitable for Retinal OCT Image Segmentation. Journal of Healthcare Engineering،Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1180941
Modern Language Association (MLA)
Luo, Su…[et al.]. The Edge Detectors Suitable for Retinal OCT Image Segmentation. Journal of Healthcare Engineering No. 2017 (2017), pp.1-13.
https://search.emarefa.net/detail/BIM-1180941
American Medical Association (AMA)
Luo, Su& Yang, Jing& Gao, Qian& Zhou, Sheng& Zhan, Chang'an A.. The Edge Detectors Suitable for Retinal OCT Image Segmentation. Journal of Healthcare Engineering. 2017. Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1180941
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1180941