Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106276
Title: A new hybrid algorithm for retinal vessels segmentation on fundus images
Authors: Dharmawan, Dhimas Arief
Li, Di
Ng, Boon Poh
Rahardja, Susanto
Keywords: CNN
DRNTU::Engineering::Electrical and electronic engineering
Fundus Images
Issue Date: 2019
Source: Dharmawan, D. A., Li, D., Ng, B. P., & Rahardja, S. (2019). A new hybrid algorithm for retinal vessels segmentation on fundus images. IEEE Access, 7, 41885-41896. doi: 10.1109/ACCESS.2019.2906344
Series/Report no.: IEEE Access
Abstract: Automatic retinal vessels segmentation is an important task in medical applications. However, most of the available retinal vessels segmentation methods are prone to poorer results when dealing with challenging situations such as detecting low-contrast micro-vessels, vessels with central reflex, and vessels in the presence of pathologies. This paper presents a new hybrid algorithm for retinal vessels segmentation on fundus images. The proposed algorithm overcomes the difficulty when dealing with the challenging situations by first applying a new directionally sensitive blood vessel enhancement method before sending fundus images to a convolutional neural network architecture derived from U-Net. To train and test the algorithm, fundus images from the DRIVE and STARE databases, as well as high-resolution fundus images from the HRF database, are utilized. In the experiment, the proposed algorithm outperforms the state-of-the-art methods in four major measures, i.e., sensitivity, F1-score, G-mean, and Mathews correlation coefficient both on the low- and high-resolution images. In addition, the proposed algorithm achieves the best connectivity-area-length score among the competing methods. Given such performance, the proposed algorithm can be adapted for vessel-like structures segmentation in other medical applications. In addition, since the new blood vessel enhancement method is independent of the U-Net model, it can be easily applied to other deep learning architectures.
URI: https://hdl.handle.net/10356/106276
http://hdl.handle.net/10220/48958
DOI: 10.1109/ACCESS.2019.2906344
Rights: © 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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