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Xue X, Wang L, Du W, Fujiwara Y, Peng Y. Multiple Preprocessing Hybrid Level Set Model for Optic Disc Segmentation in Fundus Images. SENSORS (BASEL, SWITZERLAND) 2022; 22:6899. [PMID: 36146249 PMCID: PMC9506381 DOI: 10.3390/s22186899] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 09/08/2022] [Accepted: 09/08/2022] [Indexed: 06/16/2023]
Abstract
The accurate segmentation of the optic disc (OD) in fundus images is a crucial step for the analysis of many retinal diseases. However, because of problems such as vascular occlusion, parapapillary atrophy (PPA), and low contrast, accurate OD segmentation is still a challenging task. Therefore, this paper proposes a multiple preprocessing hybrid level set model (HLSM) based on area and shape for OD segmentation. The area-based term represents the difference of average pixel values between the inside and outside of a contour, while the shape-based term measures the distance between a prior shape model and the contour. The average intersection over union (IoU) of the proposed method was 0.9275, and the average four-side evaluation (FSE) was 4.6426 on a public dataset with narrow-angle fundus images. The IoU was 0.8179 and the average FSE was 3.5946 on a wide-angle fundus image dataset compiled from a hospital. The results indicate that the proposed multiple preprocessing HLSM is effective in OD segmentation.
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Affiliation(s)
- Xiaozhong Xue
- Information and Human Science, Kyoto Institute of Technology University, Kyoto 6068585, Japan
| | - Linni Wang
- Retina & Neuron-Ophthalmology, Tianjin Medical University Eye Hospital, Tianjin 300084, China
| | - Weiwei Du
- Information and Human Science, Kyoto Institute of Technology University, Kyoto 6068585, Japan
| | - Yusuke Fujiwara
- Information and Human Science, Kyoto Institute of Technology University, Kyoto 6068585, Japan
| | - Yahui Peng
- School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
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Addis V, Chen M, Zorger R, Salowe R, Daniel E, Lee R, Pistilli M, Gao J, Maguire MG, Chan L, Gudiseva HV, Zenebe-Gete S, Merriam S, Smith EJ, Martin R, Parker Ostroff C, Gee JC, Cui QN, Miller-Ellis E, O’Brien JM, Sankar PS. A Precise Method to Evaluate 360 Degree Measures of Optic Cup and Disc Morphology in an African American Cohort and Its Genetic Applications. Genes (Basel) 2021; 12:genes12121961. [PMID: 34946910 PMCID: PMC8701339 DOI: 10.3390/genes12121961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/01/2021] [Accepted: 12/05/2021] [Indexed: 11/16/2022] Open
Abstract
(1) Background: Vertical cup-to-disc ratio (CDR) is an important measure for evaluating damage to the optic nerve head (ONH) in glaucoma patients. However, this measure often does not fully capture the irregular cupping observed in glaucomatous nerves. We developed and evaluated a method to measure cup-to-disc ratio (CDR) at all 360 degrees of the ONH. (2) Methods: Non-physician graders from the Scheie Reading Center outlined the cup and disc on digital stereo color disc images from African American patients enrolled in the Primary Open-Angle African American Glaucoma Genetics (POAAGG) study. After converting the resultant coordinates into polar representation, the CDR at each 360-degree location of the ONH was obtained. We compared grader VCDR values with clinical VCDR values, using Spearman correlation analysis, and validated significant genetic associations with clinical VCDR, using grader VCDR values. (3) Results: Graders delineated outlines of the cup contour and disc boundaries twice in each of 1815 stereo disc images. For both cases and controls, the mean CDR was highest at the horizontal bisector, particularly in the temporal region, as compared to other degree locations. There was a good correlation between grader CDR at the vertical bisector and clinical VCDR (Spearman Correlation OD: r = 0.78 [95% CI: 0.76–0.79]). An SNP in the MPDZ gene, associated with clinical VCDR in a prior genome-wide association study, showed a significant association with grader VCDR (p = 0.01) and grader CDR area ratio (p = 0.02). (4) Conclusions: The CDR of both glaucomatous and non-glaucomatous eyes varies by degree location, with the highest measurements in the temporal region of the eye. This method can be useful for capturing innate eccentric ONH morphology, tracking disease progression, and identifying genetic associations.
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Affiliation(s)
- Victoria Addis
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; (V.A.); (R.S.); (E.D.); (R.L.); (M.P.); (J.G.); (M.G.M.); (L.C.); (H.V.G.); (S.Z.-G.); (S.M.); (E.J.S.); (R.M.); (C.P.O.); (Q.N.C.); (E.M.-E.); (P.S.S.)
| | - Min Chen
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; (M.C.); (J.C.G.)
| | - Richard Zorger
- Penn Vision Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Rebecca Salowe
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; (V.A.); (R.S.); (E.D.); (R.L.); (M.P.); (J.G.); (M.G.M.); (L.C.); (H.V.G.); (S.Z.-G.); (S.M.); (E.J.S.); (R.M.); (C.P.O.); (Q.N.C.); (E.M.-E.); (P.S.S.)
| | - Ebenezer Daniel
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; (V.A.); (R.S.); (E.D.); (R.L.); (M.P.); (J.G.); (M.G.M.); (L.C.); (H.V.G.); (S.Z.-G.); (S.M.); (E.J.S.); (R.M.); (C.P.O.); (Q.N.C.); (E.M.-E.); (P.S.S.)
| | - Roy Lee
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; (V.A.); (R.S.); (E.D.); (R.L.); (M.P.); (J.G.); (M.G.M.); (L.C.); (H.V.G.); (S.Z.-G.); (S.M.); (E.J.S.); (R.M.); (C.P.O.); (Q.N.C.); (E.M.-E.); (P.S.S.)
| | - Maxwell Pistilli
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; (V.A.); (R.S.); (E.D.); (R.L.); (M.P.); (J.G.); (M.G.M.); (L.C.); (H.V.G.); (S.Z.-G.); (S.M.); (E.J.S.); (R.M.); (C.P.O.); (Q.N.C.); (E.M.-E.); (P.S.S.)
| | - Jinpeng Gao
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; (V.A.); (R.S.); (E.D.); (R.L.); (M.P.); (J.G.); (M.G.M.); (L.C.); (H.V.G.); (S.Z.-G.); (S.M.); (E.J.S.); (R.M.); (C.P.O.); (Q.N.C.); (E.M.-E.); (P.S.S.)
| | - Maureen G. Maguire
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; (V.A.); (R.S.); (E.D.); (R.L.); (M.P.); (J.G.); (M.G.M.); (L.C.); (H.V.G.); (S.Z.-G.); (S.M.); (E.J.S.); (R.M.); (C.P.O.); (Q.N.C.); (E.M.-E.); (P.S.S.)
| | - Lilian Chan
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; (V.A.); (R.S.); (E.D.); (R.L.); (M.P.); (J.G.); (M.G.M.); (L.C.); (H.V.G.); (S.Z.-G.); (S.M.); (E.J.S.); (R.M.); (C.P.O.); (Q.N.C.); (E.M.-E.); (P.S.S.)
| | - Harini V. Gudiseva
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; (V.A.); (R.S.); (E.D.); (R.L.); (M.P.); (J.G.); (M.G.M.); (L.C.); (H.V.G.); (S.Z.-G.); (S.M.); (E.J.S.); (R.M.); (C.P.O.); (Q.N.C.); (E.M.-E.); (P.S.S.)
| | - Selam Zenebe-Gete
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; (V.A.); (R.S.); (E.D.); (R.L.); (M.P.); (J.G.); (M.G.M.); (L.C.); (H.V.G.); (S.Z.-G.); (S.M.); (E.J.S.); (R.M.); (C.P.O.); (Q.N.C.); (E.M.-E.); (P.S.S.)
| | - Sayaka Merriam
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; (V.A.); (R.S.); (E.D.); (R.L.); (M.P.); (J.G.); (M.G.M.); (L.C.); (H.V.G.); (S.Z.-G.); (S.M.); (E.J.S.); (R.M.); (C.P.O.); (Q.N.C.); (E.M.-E.); (P.S.S.)
| | - Eli J. Smith
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; (V.A.); (R.S.); (E.D.); (R.L.); (M.P.); (J.G.); (M.G.M.); (L.C.); (H.V.G.); (S.Z.-G.); (S.M.); (E.J.S.); (R.M.); (C.P.O.); (Q.N.C.); (E.M.-E.); (P.S.S.)
| | - Revell Martin
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; (V.A.); (R.S.); (E.D.); (R.L.); (M.P.); (J.G.); (M.G.M.); (L.C.); (H.V.G.); (S.Z.-G.); (S.M.); (E.J.S.); (R.M.); (C.P.O.); (Q.N.C.); (E.M.-E.); (P.S.S.)
| | - Candace Parker Ostroff
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; (V.A.); (R.S.); (E.D.); (R.L.); (M.P.); (J.G.); (M.G.M.); (L.C.); (H.V.G.); (S.Z.-G.); (S.M.); (E.J.S.); (R.M.); (C.P.O.); (Q.N.C.); (E.M.-E.); (P.S.S.)
| | - James C. Gee
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; (M.C.); (J.C.G.)
| | - Qi N. Cui
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; (V.A.); (R.S.); (E.D.); (R.L.); (M.P.); (J.G.); (M.G.M.); (L.C.); (H.V.G.); (S.Z.-G.); (S.M.); (E.J.S.); (R.M.); (C.P.O.); (Q.N.C.); (E.M.-E.); (P.S.S.)
| | - Eydie Miller-Ellis
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; (V.A.); (R.S.); (E.D.); (R.L.); (M.P.); (J.G.); (M.G.M.); (L.C.); (H.V.G.); (S.Z.-G.); (S.M.); (E.J.S.); (R.M.); (C.P.O.); (Q.N.C.); (E.M.-E.); (P.S.S.)
| | - Joan M. O’Brien
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; (V.A.); (R.S.); (E.D.); (R.L.); (M.P.); (J.G.); (M.G.M.); (L.C.); (H.V.G.); (S.Z.-G.); (S.M.); (E.J.S.); (R.M.); (C.P.O.); (Q.N.C.); (E.M.-E.); (P.S.S.)
- Correspondence: Joan.O’; Tel.: +1-215-662-8657; Fax: +1-215-662-9676
| | - Prithvi S. Sankar
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; (V.A.); (R.S.); (E.D.); (R.L.); (M.P.); (J.G.); (M.G.M.); (L.C.); (H.V.G.); (S.Z.-G.); (S.M.); (E.J.S.); (R.M.); (C.P.O.); (Q.N.C.); (E.M.-E.); (P.S.S.)
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Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2019; 2019:8973287. [PMID: 31827591 PMCID: PMC6886352 DOI: 10.1155/2019/8973287] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2019] [Revised: 09/26/2019] [Accepted: 10/22/2019] [Indexed: 11/17/2022]
Abstract
Accurate optic disc and optic cup segmentation plays an important role for diagnosing glaucoma. However, most existing segmentation approaches suffer from the following limitations. On the one hand, image devices or illumination variations always lead to intensity inhomogeneity in the fundus image. On the other hand, the spatial prior knowledge of optic disc and optic cup, e.g., the optic cup is always contained inside the optic disc region, is ignored. Therefore, the effectiveness of segmentation approaches is greatly reduced. Different from most previous approaches, we present a novel locally statistical active contour model with the structure prior (LSACM-SP) approach to jointly and robustly segment the optic disc and optic cup structures. First, some preprocessing techniques are used to automatically extract initial contour of object. Then, we introduce the locally statistical active contour model (LSACM) to optic disc and optic cup segmentation in the presence of intensity inhomogeneity. Finally, taking the specific morphology of optic disc and optic cup into consideration, a novel structure prior is proposed to guide the model to generate accurate segmentation results. Experimental results demonstrate the advantage and superiority of our approach on two publicly available databases, i.e., DRISHTI-GS and RIM-ONE r2, by comparing with some well-known algorithms.
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A coarse-to-fine deep learning framework for optic disc segmentation in fundus images. Biomed Signal Process Control 2019; 51:82-89. [PMID: 33850515 DOI: 10.1016/j.bspc.2019.01.022] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Accurate segmentation of the optic disc (OD) depicted on color fundus images may aid in the early detection and quantitative diagnosis of retinal diseases, such as glaucoma and optic atrophy. In this study, we proposed a coarse-to-fine deep learning framework on the basis of a classical convolutional neural network (CNN), known as the U-net model, to accurately identify the optic disc. This network was trained separately on color fundus images and their grayscale vessel density maps, leading to two different segmentation results from the entire image. We combined the results using an overlap strategy to identify a local image patch (disc candidate region), which was then fed into the U-net model for further segmentation. Our experiments demonstrated that the developed framework achieved an average intersection over union (IoU) and a dice similarity coefficient (DSC) of 89.1% and 93.9%, respectively, based on 2,978 test images from our collected dataset and six public datasets, as compared to 87.4% and 92.5% obtained by only using the sole U-net model. The comparison with available approaches demonstrated a reliable and relatively high performance of the proposed deep learning framework in automated OD segmentation.
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Haleem MS, Han L, Hemert JV, Li B, Fleming A, Pasquale LR, Song BJ. A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis. J Med Syst 2017; 42:20. [PMID: 29218460 PMCID: PMC5719827 DOI: 10.1007/s10916-017-0859-4] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2017] [Accepted: 11/07/2017] [Indexed: 11/26/2022]
Abstract
This paper proposes a novel Adaptive Region-based Edge Smoothing Model (ARESM) for automatic boundary detection of optic disc and cup to aid automatic glaucoma diagnosis. The novelty of our approach consists of two aspects: 1) automatic detection of initial optimum object boundary based on a Region Classification Model (RCM) in a pixel-level multidimensional feature space; 2) an Adaptive Edge Smoothing Update model (AESU) of contour points (e.g. misclassified or irregular points) based on iterative force field calculations with contours obtained from the RCM by minimising energy function (an approach that does not require predefined geometric templates to guide auto-segmentation). Such an approach provides robustness in capturing a range of variations and shapes. We have conducted a comprehensive comparison between our approach and the state-of-the-art existing deformable models and validated it with publicly available datasets. The experimental evaluation shows that the proposed approach significantly outperforms existing methods. The generality of the proposed approach will enable segmentation and detection of other object boundaries and provide added value in the field of medical image processing and analysis.
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Affiliation(s)
- Muhammad Salman Haleem
- School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester, M1 5GD UK
| | - Liangxiu Han
- School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester, M1 5GD UK
| | - Jano van Hemert
- Optos Plc, Queensferry House, Carnegie Business Campus, Enterprise Way, Dunfermline, Scotland, KY11 8GR UK
| | - Baihua Li
- Department of Computer Science, Loughborough University, Loughborough, LE11 3TU UK
| | - Alan Fleming
- Optos Plc, Queensferry House, Carnegie Business Campus, Enterprise Way, Dunfermline, Scotland, KY11 8GR UK
| | - Louis R. Pasquale
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA USA
| | - Brian J. Song
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA USA
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Almazroa A, Sun W, Alodhayb S, Raahemifar K, Lakshminarayanan V. Optic disc segmentation for glaucoma screening system using fundus images. Clin Ophthalmol 2017; 11:2017-2029. [PMID: 29180847 PMCID: PMC5695265 DOI: 10.2147/opth.s140061] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Segmenting the optic disc (OD) is an important and essential step in creating a frame of reference for diagnosing optic nerve head pathologies such as glaucoma. Therefore, a reliable OD segmentation technique is necessary for automatic screening of optic nerve head abnormalities. The main contribution of this paper is in presenting a novel OD segmentation algorithm based on applying a level set method on a localized OD image. To prevent the blood vessels from interfering with the level set process, an inpainting technique was applied. As well an important contribution was to involve the variations in opinions among the ophthalmologists in detecting the disc boundaries and diagnosing the glaucoma. Most of the previous studies were trained and tested based on only one opinion, which can be assumed to be biased for the ophthalmologist. In addition, the accuracy was calculated based on the number of images that coincided with the ophthalmologists’ agreed-upon images, and not only on the overlapping images as in previous studies. The ultimate goal of this project is to develop an automated image processing system for glaucoma screening. The disc algorithm is evaluated using a new retinal fundus image dataset called RIGA (retinal images for glaucoma analysis). In the case of low-quality images, a double level set was applied, in which the first level set was considered to be localization for the OD. Five hundred and fifty images are used to test the algorithm accuracy as well as the agreement among the manual markings of six ophthalmologists. The accuracy of the algorithm in marking the optic disc area and centroid was 83.9%, and the best agreement was observed between the results of the algorithm and manual markings in 379 images.
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Affiliation(s)
- Ahmed Almazroa
- King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia.,Ophthalmology and Visual Science Department, University of Michigan, Ann Arbor, MI, USA
| | - Weiwei Sun
- School of Resource and Environmental Sciences, Wuhan University, Wuchang, Wuhan, Hubei, China
| | | | - Kaamran Raahemifar
- Department of Electrical and Computer Engineering, University of Ryerson, Toronto, ON
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Miri MS, Abràmoff MD, Kwon YH, Sonka M, Garvin MK. A machine-learning graph-based approach for 3D segmentation of Bruch's membrane opening from glaucomatous SD-OCT volumes. Med Image Anal 2017; 39:206-217. [PMID: 28528295 DOI: 10.1016/j.media.2017.04.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Revised: 04/24/2017] [Accepted: 04/26/2017] [Indexed: 01/26/2023]
Abstract
Bruch's membrane opening-minimum rim width (BMO-MRW) is a recently proposed structural parameter which estimates the remaining nerve fiber bundles in the retina and is superior to other conventional structural parameters for diagnosing glaucoma. Measuring this structural parameter requires identification of BMO locations within spectral domain-optical coherence tomography (SD-OCT) volumes. While most automated approaches for segmentation of the BMO either segment the 2D projection of BMO points or identify BMO points in individual B-scans, in this work, we propose a machine-learning graph-based approach for true 3D segmentation of BMO from glaucomatous SD-OCT volumes. The problem is formulated as an optimization problem for finding a 3D path within the SD-OCT volume. In particular, the SD-OCT volumes are transferred to the radial domain where the closed loop BMO points in the original volume form a path within the radial volume. The estimated location of BMO points in 3D are identified by finding the projected location of BMO points using a graph-theoretic approach and mapping the projected locations onto the Bruch's membrane (BM) surface. Dynamic programming is employed in order to find the 3D BMO locations as the minimum-cost path within the volume. In order to compute the cost function needed for finding the minimum-cost path, a random forest classifier is utilized to learn a BMO model, obtained by extracting intensity features from the volumes in the training set, and computing the required 3D cost function. The proposed method is tested on 44 glaucoma patients and evaluated using manual delineations. Results show that the proposed method successfully identifies the 3D BMO locations and has significantly smaller errors compared to the existing 3D BMO identification approaches.
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Affiliation(s)
- Mohammad Saleh Miri
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, United States
| | - Michael D Abràmoff
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, United States; Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, 52242, United States; Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, 52242, United States; Iowa City VA Health Care System, Iowa City, IA, 52246, United States
| | - Young H Kwon
- Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, 52242, United States
| | - Milan Sonka
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, United States; Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, 52242, United States
| | - Mona K Garvin
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, United States; Iowa City VA Health Care System, Iowa City, IA, 52246, United States.
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Optic Disc and Optic Cup Segmentation Methodologies for Glaucoma Image Detection: A Survey. J Ophthalmol 2015; 2015:180972. [PMID: 26688751 PMCID: PMC4673359 DOI: 10.1155/2015/180972] [Citation(s) in RCA: 121] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Accepted: 09/27/2015] [Indexed: 01/27/2023] Open
Abstract
Glaucoma is the second leading cause of loss of vision in the world. Examining the head of optic nerve (cup-to-disc ratio) is very important for diagnosing glaucoma and for patient monitoring after diagnosis. Images of optic disc and optic cup are acquired by fundus camera as well as Optical Coherence Tomography. The optic disc and optic cup segmentation techniques are used to isolate the relevant parts of the retinal image and to calculate the cup-to-disc ratio. The main objective of this paper is to review segmentation methodologies and techniques for the disc and cup boundaries which are utilized to calculate the disc and cup geometrical parameters automatically and accurately to help the professionals in the glaucoma to have a wide view and more details about the optic nerve head structure using retinal fundus images. We provide a brief description of each technique, highlighting its classification and performance metrics. The current and future research directions are summarized and discussed.
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