Next Article in Journal
Pipeline Leak Localization Based on FBG Hoop Strain Sensors Combined with BP Neural Network
Previous Article in Journal
Enhanced Effective Filtering Approach (eEFA) for Improving HSR Network Performance in Smart Grids
Open AccessReview

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.
Appl. Sci. 2018, 8(2), 155; https://doi.org/10.3390/app8020155
Received: 27 December 2017 / Revised: 17 January 2018 / Accepted: 19 January 2018 / Published: 23 January 2018
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
Show Figures

Figure 1

MDPI and ACS Style

Almotiri, J.; Elleithy, K.; Elleithy, A. Retinal Vessels Segmentation Techniques and Algorithms: A Survey. Appl. Sci. 2018, 8, 155. https://doi.org/10.3390/app8020155

AMA Style

Almotiri J, Elleithy K, Elleithy A. Retinal Vessels Segmentation Techniques and Algorithms: A Survey. Applied Sciences. 2018; 8(2):155. https://doi.org/10.3390/app8020155

Chicago/Turabian Style

Almotiri, Jasem; Elleithy, Khaled; Elleithy, Abdelrahman. 2018. "Retinal Vessels Segmentation Techniques and Algorithms: A Survey" Appl. Sci. 8, no. 2: 155. https://doi.org/10.3390/app8020155

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop