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Appl. Sci. 2018, 8(2), 155; https://doi.org/10.3390/app8020155

Retinal Vessels Segmentation Techniques and Algorithms: A Survey

1
Computer Science and Engineering Department, University of Bridgeport, 126 Park Ave, Bridgeport, CT 06604, USA
2
Computer Science Department, William Paterson University, 300 Pompton Rd, Wayne, NJ 07470, USA
*
Author to whom correspondence should be addressed.
Received: 27 December 2017 / Revised: 17 January 2018 / Accepted: 19 January 2018 / Published: 23 January 2018
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Abstract

Retinal vessels identification and localization aim to separate the different retinal vasculature structure tissues, either wide or narrow ones, from the fundus image background and other retinal anatomical structures such as optic disc, macula, and abnormal lesions. Retinal vessels identification studies are attracting more and more attention in recent years due to non-invasive fundus imaging and the crucial information contained in vasculature structure which is helpful for the detection and diagnosis of a variety of retinal pathologies included but not limited to: Diabetic Retinopathy (DR), glaucoma, hypertension, and Age-related Macular Degeneration (AMD). With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting retinal vessels are becoming more and more crucial and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for retinal vessels segmentation techniques. Firstly, a brief introduction to retinal fundus photography and imaging modalities of retinal images is given. Then, the preprocessing operations and the state of the art methods of retinal vessels identification are introduced. Moreover, the evaluation and validation of the results of retinal vessels segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for retinal vessels identification techniques. View Full-Text
Keywords: retinal vessels segmentation; matched filters; fuzzy expert systems; fuzzy c means; machine learning; adaptive thresholding; mathematical morphology; level set; vessel tracking; multi-scaling retinal vessels segmentation; matched filters; fuzzy expert systems; fuzzy c means; machine learning; adaptive thresholding; mathematical morphology; level set; vessel tracking; multi-scaling
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Almotiri, J.; Elleithy, K.; Elleithy, A. Retinal Vessels Segmentation Techniques and Algorithms: A Survey. Appl. Sci. 2018, 8, 155.

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