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Zhao W, Zhang Z, Wang Z, Guo Y, Xie J, Xu X. ECLNet: Center localization of eye structures based on Adaptive Gaussian Ellipse Heatmap. Comput Biol Med 2023; 153:106485. [PMID: 36586229 DOI: 10.1016/j.compbiomed.2022.106485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 12/17/2022] [Accepted: 12/25/2022] [Indexed: 12/29/2022]
Abstract
Accurately localizing the center of specific biological structures in medical images is of great significance for clinical treatment. The center localization task can be viewed as an estimation problem of keypoints, and the heatmap is often used to describe the probability of the location of keypoints during estimation. Existing methods construct the heatmap from a Gaussian kernel function with a fixed standard deviation, therefore cannot adapt to morphologic changes of the target region. In this paper, we build a deep network, ECLNet, to localize the center of eye-related structures in medical images. Meanwhile, we propose a method called Adaptive Gaussian Ellipse Heatmap (AGEH), which can efficiently utilize the gradient feature of the target region to adjust the morphology of the heatmap. The ECLNet localizes the optic disc and fovea center with mean Euclidean Distance of 17.995 and 39.446 pixels, respectively, for IDRiD dataset. The ECLNet also successfully localizes the eye center with the mean absolute Position Error of 0.186±0.027 mm for CATARACT dataset. The results show that our proposed method has a better performance compared with some state-of-the-art methods.
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Affiliation(s)
- Wentao Zhao
- College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, 030024, China.
| | - Zhe Zhang
- College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Zhao Wang
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Yan Guo
- Beijing University of Chinese Medicine Third Affiliated Hospital, Beijing, 100029, China
| | - Jun Xie
- College of Information and Computer, Taiyuan University of Technology, Jinzhong, 030600, China
| | - Xinying Xu
- College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, 030024, China.
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2
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Zaaboub N, Sandid F, Douik A, Solaiman B. Optic disc detection and segmentation using saliency mask in retinal fundus images. Comput Biol Med 2022; 150:106067. [PMID: 36150251 DOI: 10.1016/j.compbiomed.2022.106067] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 08/25/2022] [Accepted: 08/27/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND AND OBJECTIVE Detection of the Optic Disc (OD) in retinal fundus image is crucial in identifying diverse abnormal conditions in the retina such as diabetic retinopathy. Previous systems are oriented to the OD detection and segmentation. Most research failed to locate the OD in the case when the image does not have a criterion appearance. The objective of the proposed work is to precisely define a new and robust OD segmentation in color retinal fundus images. METHODS The proposed algorithm is composed of two stages: OD localization and segmentation. The first phase consists in the OD localization through: 1) a preprocessing step; 2) vessel extraction and elimination, and 3) a geometric analysis allowing to decide the OD location. For the second phase, a set of is computed in order to produce various candidates. A combination of these candidates accurately forms a completed contour of the OD. RESULTS The proposed method is evaluated using 10 publicly available databases as well as a local database. Accuracy rates in the RimOne and IDRID databases are 98.06% and 99.71%, respectively, and 100% for the Chase, Drive, HRF, Drishti, Drions, Bin Rushed, Magrabia, Messidor and LocalDB databases with an overall success rate of 99.80% and specificity rates of 99.44%, 99.64%, 99.66%, 99.66%, 99.70%, 99.87%, 99.72%, 99.83% and 99.82% for the Rim One, Drions, IDRID, Drishti, HRF, Bin Rushed, Magrabia, Messidor and proprietary databases. CONCLUSION The main advantage of the proposed approach is the robustness and the excellent performances even with critical cases of retinal images. The proposed method achieves the state-of-the-art performances with regards to the OD detection and segmentation. It is also of a great interest for clinical usage without the expert intervention to treat each image.
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Affiliation(s)
- Nihal Zaaboub
- ENIT: National Engineering School of Tunis, University Tunis El Manar, Tunisia; NOCCS-ENISo: Networked Objects Control and Communication Systems Laboratory, Tunisia.
| | - Faten Sandid
- NOCCS-ENISo: Networked Objects Control and Communication Systems Laboratory, Tunisia
| | - Ali Douik
- NOCCS-ENISo: Networked Objects Control and Communication Systems Laboratory, Tunisia; ENISo: National Engineering School of Sousse, University of Sousse, Tunisia
| | - Basel Solaiman
- Image & Information Processing Department (iTi), IMT-Atlantique, Technopôle Brest Iroise CS 83818, 29238 Brest, France
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3
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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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4
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Mahmood MT, Lee IH. Optic Disc Localization in Fundus Images through Accumulated Directional and Radial Blur Analysis. Comput Med Imaging Graph 2022; 98:102058. [DOI: 10.1016/j.compmedimag.2022.102058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 10/29/2021] [Accepted: 03/17/2022] [Indexed: 10/18/2022]
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5
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Toptaş B, Toptaş M, Hanbay D. Detection of Optic Disc Localization from Retinal Fundus Image Using Optimized Color Space. J Digit Imaging 2022; 35:302-319. [PMID: 35018540 PMCID: PMC8921449 DOI: 10.1007/s10278-021-00566-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 11/25/2021] [Accepted: 12/06/2021] [Indexed: 10/19/2022] Open
Abstract
Optic disc localization offers an important clue in detecting other retinal components such as the macula, fovea, and retinal vessels. With the correct detection of this area, sudden vision loss caused by diseases such as age-related macular degeneration and diabetic retinopathy can be prevented. Therefore, there is an increase in computer-aided diagnosis systems in this field. In this paper, an automated method for detecting optic disc localization is proposed. In the proposed method, the fundus images are moved from RGB color space to a new color space by using an artificial bee colony algorithm. In the new color space, the localization of the optical disc is clearer than in the RGB color space. In this method, a matrix called the feature matrix is created. This matrix is obtained from the color pixel values of the image patches containing the optical disc and the image patches not containing the optical disc. Then, the conversion matrix is created. The initial values of this matrix are randomly determined. These two matrices are processed in the artificial bee colony algorithm. Ultimately, the conversion matrix becomes optimal and is applied over the original fundus images. Thus, the images are moved to the new color space. Thresholding is applied to these images, and the optic disc localization is obtained. The success rate of the proposed method has been tested on three general datasets. The accuracy success rate for the DRIVE, DRIONS, and MESSIDOR datasets, respectively, is 100%, 96.37%, and 94.42% for the proposed method.
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Affiliation(s)
- Buket Toptaş
- Computer Eng. Dept, Engineering and Natural Science Faculty, Bandırma Onyedi Eylül University, Balıkesir, Turkey.
| | - Murat Toptaş
- Software Eng. Dept, Engineering and Natural Science Faculty, Bandırma Onyedi Eylül University, Balıkesir, Turkey
| | - Davut Hanbay
- Computer Eng. Dept., Engineering Faculty, Inonu University, 44280, Malatya, Turkey
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Krishna Adithya V, Williams BM, Czanner S, Kavitha S, Friedman DS, Willoughby CE, Venkatesh R, Czanner G. EffUnet-SpaGen: An Efficient and Spatial Generative Approach to Glaucoma Detection. J Imaging 2021; 7:92. [PMID: 39080880 PMCID: PMC8321378 DOI: 10.3390/jimaging7060092] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/21/2021] [Accepted: 05/27/2021] [Indexed: 12/11/2022] Open
Abstract
Current research in automated disease detection focuses on making algorithms "slimmer" reducing the need for large training datasets and accelerating recalibration for new data while achieving high accuracy. The development of slimmer models has become a hot research topic in medical imaging. In this work, we develop a two-phase model for glaucoma detection, identifying and exploiting a redundancy in fundus image data relating particularly to the geometry. We propose a novel algorithm for the cup and disc segmentation "EffUnet" with an efficient convolution block and combine this with an extended spatial generative approach for geometry modelling and classification, termed "SpaGen" We demonstrate the high accuracy achievable by EffUnet in detecting the optic disc and cup boundaries and show how our algorithm can be quickly trained with new data by recalibrating the EffUnet layer only. Our resulting glaucoma detection algorithm, "EffUnet-SpaGen", is optimized to significantly reduce the computational burden while at the same time surpassing the current state-of-art in glaucoma detection algorithms with AUROC 0.997 and 0.969 in the benchmark online datasets ORIGA and DRISHTI, respectively. Our algorithm also allows deformed areas of the optic rim to be displayed and investigated, providing explainability, which is crucial to successful adoption and implementation in clinical settings.
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Affiliation(s)
- Venkatesh Krishna Adithya
- Department of Glaucoma, Aravind Eye Care System, Thavalakuppam, Pondicherry 605007, India; (V.K.A.); (S.K.); (R.V.)
| | - Bryan M. Williams
- School of Computing and Communications, Lancaster University, Bailrigg, Lancaster LA1 4WA, UK;
| | - Silvester Czanner
- School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool L3 3AF, UK;
| | - Srinivasan Kavitha
- Department of Glaucoma, Aravind Eye Care System, Thavalakuppam, Pondicherry 605007, India; (V.K.A.); (S.K.); (R.V.)
| | - David S. Friedman
- Glaucoma Center of Excellence, Harvard Medical School, Boston, MA 02114, USA;
| | - Colin E. Willoughby
- Biomedical Research Institute, Ulster University, Coleraine, Co. Londonderry BT52 1SA, UK;
| | - Rengaraj Venkatesh
- Department of Glaucoma, Aravind Eye Care System, Thavalakuppam, Pondicherry 605007, India; (V.K.A.); (S.K.); (R.V.)
| | - Gabriela Czanner
- School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool L3 3AF, UK;
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7
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Xie H, Tang C, Xu M, Lei Z. Improved SSD network for accurate detection of optic disc and fovea and application in excyclotropia screening. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2021; 38:10-18. [PMID: 33362147 DOI: 10.1364/josaa.403850] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 11/11/2020] [Indexed: 06/12/2023]
Abstract
The detection of the optic disc (OD) and fovea is essential to many automatic diagnosis systems for retinal diseases. The single shot multibox detector (SSD) can generate predictions from feature maps of various resolutions, which has not been introduced into the OD and fovea detection. To enhance the detection performance, we propose an improved SSD network, which has strengthened information flow enabled by the dense connections. The proposed method can achieve multiscale detection of the OD and fovea with strengthened feature propagation. Extensive experiments on the publicly available Messidor database and local fundus images are performed to evaluate the performance of the proposed method. Compared with seven types of representative solutions in the Messidor database, the proposed method can achieve competitive performance compared to state-of-the-art algorithms. Furthermore, the proposed method is applied to the excyclotropia screening. The screening results demonstrate promising application prospects for the proposed method in medical practice.
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8
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Romero-Oraá R, García M, Oraá-Pérez J, López MI, Hornero R. A robust method for the automatic location of the optic disc and the fovea in fundus images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 196:105599. [PMID: 32574904 DOI: 10.1016/j.cmpb.2020.105599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 06/01/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE The location of the optic disc (OD) and the fovea is usually crucial in automatic screening systems for diabetic retinopathy. Previous methods aimed at their location often fail when these structures do not have the standard appearance. The purpose of this work is to propose novel, robust methods for the automatic detection of the OD and the fovea. METHODS The proposed method comprises a preprocessing stage, a method for retinal background extraction, a vasculature segmentation phase and the computation of various novel saliency maps. The main novelty of this work is the combination of the proposed saliency maps, which represent the spatial relationships between some structures of the retina and the visual appearance of the OD and fovea. Another contribution is the method to extract the retinal background, based on region-growing. RESULTS The proposed methods were evaluated over a proprietary database and three public databases: DRIVE, DiaretDB1 and Messidor. For the OD, we achieved 100% accuracy for all databases except Messidor (99.50%). As for the fovea location, we also reached 100% accuracy for all databases except Messidor (99.67%). CONCLUSIONS Our results suggest that the proposed methods are robust and effective to automatically detect the OD and the fovea. This way, they can be useful in automatic screening systems for diabetic retinopathy as well as other retinal diseases.
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Affiliation(s)
- Roberto Romero-Oraá
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo Belén 15, Valladolid 47011, Spain.; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain.
| | - María García
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo Belén 15, Valladolid 47011, Spain.; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain.
| | - Javier Oraá-Pérez
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo Belén 15, Valladolid 47011, Spain..
| | - María I López
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo Belén 15, Valladolid 47011, Spain.; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain; Department of Ophthalmology, Hospital Clínico Universitario de Valladolid, Valladolid 47003, Spain.; Instituto Universitario de Oftalmobiología Aplicada (IOBA), Universidad de Valladolid, Valladolid 47011, Spain..
| | - Roberto Hornero
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo Belén 15, Valladolid 47011, Spain.; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain; Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, Valladolid 47011, Spain..
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9
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Intelligent optic disc segmentation using improved particle swarm optimization and evolving ensemble models. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106328] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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10
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Simultaneous segmentation of the optic disc and fovea in retinal images using evolutionary algorithms. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05060-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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11
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Ramachandran S, Kochitty S, Vinekar A, John R. A fully convolutional neural network approach for the localization of optic disc in retinopathy of prematurity diagnosis. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-179708] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Affiliation(s)
- Sivakumar Ramachandran
- Department of Electronics and Communication, College of Engineering Trivandrum, Kerala, India
| | - Shymol Kochitty
- Department of Electronics and Communication, College of Engineering Trivandrum, Kerala, India
| | - Anand Vinekar
- Department of Pediatric and Tele-ROP Services, Narayana Nethralaya Eye Hospital, Bangalore, India
| | - Renu John
- Department of Biomedical Engineering, Indian Institute of Technology Hyderabad, Telangana, India
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12
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Dharmawan DA, Ng BP, Rahardja S. A new optic disc segmentation method using a modified Dolph-Chebyshev matched filter. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101932] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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13
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Weakly Supervised and Semi-Supervised Semantic Segmentation for Optic Disc of Fundus Image. Symmetry (Basel) 2020. [DOI: 10.3390/sym12010145] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Weakly supervised and semi-supervised semantic segmentation has been widely used in the field of computer vision. Since it does not require groundtruth or it only needs a small number of groundtruths for training. Recently, some works use pseudo groundtruths which are generated by a classified network to train the model, however, this method is not suitable for medical image segmentation. To tackle this challenging problem, we use the GrabCut method to generate the pseudo groundtruths in this paper, and then we train the network based on a modified U-net model with the generated pseudo groundtruths, finally we utilize a small amount of groundtruths to fine tune the model. Extensive experiments on the challenging RIM-ONE and DRISHTI-GS benchmarks strongly demonstrate the effectiveness of our algorithm. We obtain state-of-art results on RIM-ONE and DRISHTI-GS databases.
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14
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Automated detection of optic disc contours in fundus images using decision tree classifier. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2019.11.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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15
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Accurate Optic Disc and Cup Segmentation from Retinal Images Using a Multi-Feature Based Approach for Glaucoma Assessment. Symmetry (Basel) 2019. [DOI: 10.3390/sym11101267] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Accurate optic disc (OD) and optic cup (OC) segmentation play a critical role in automatic glaucoma diagnosis. In this paper, we present an automatic segmentation technique regarding the OD and the OC for glaucoma assessment. First, the robust adaptive approach for initializing the level set is designed to increase the performance of contour evolution. Afterwards, in order to handle the complex OD appearance affected by intensity inhomogeneity, pathological changes, and vessel occlusion, a novel model that integrates ample information of OD with the effective local intensity clustering (LIC) model together is presented. For the OC segmentation, to overcome the segmentation challenge as the OC’s complex anatomy location, a novel preprocessing method based on structure prior information between the OD and the OC is designed to guide contour evolution in an effective region. Then, a novel implicit region based on modified data term using a richer form of local image clustering information at each point of interest gathered over a multiple-channel feature image space is presented, to enhance the robustness of the variations found in and around the OC region. The presented models symmetrically integrate the information at each point in a single-channel image from a multiple-channel feature image space. Thus, these models correlate with the concept of symmetry. The proposed models are tested on the publicly available DRISHTI-GS database and the experimental results demonstrate that the models outperform state-of-the-art methods.
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16
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Porwal P, Pachade S, Kokare M, Deshmukh G, Son J, Bae W, Liu L, Wang J, Liu X, Gao L, Wu T, Xiao J, Wang F, Yin B, Wang Y, Danala G, He L, Choi YH, Lee YC, Jung SH, Li Z, Sui X, Wu J, Li X, Zhou T, Toth J, Baran A, Kori A, Chennamsetty SS, Safwan M, Alex V, Lyu X, Cheng L, Chu Q, Li P, Ji X, Zhang S, Shen Y, Dai L, Saha O, Sathish R, Melo T, Araújo T, Harangi B, Sheng B, Fang R, Sheet D, Hajdu A, Zheng Y, Mendonça AM, Zhang S, Campilho A, Zheng B, Shen D, Giancardo L, Quellec G, Mériaudeau F. IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge. Med Image Anal 2019; 59:101561. [PMID: 31671320 DOI: 10.1016/j.media.2019.101561] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 09/09/2019] [Accepted: 09/16/2019] [Indexed: 02/07/2023]
Abstract
Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is challenging due to the scarcity of medical professionals able to screen a growing global diabetic population at risk for DR. Computer-aided disease diagnosis in retinal image analysis could provide a sustainable approach for such large-scale screening effort. The recent scientific advances in computing capacity and machine learning approaches provide an avenue for biomedical scientists to reach this goal. Aiming to advance the state-of-the-art in automatic DR diagnosis, a grand challenge on "Diabetic Retinopathy - Segmentation and Grading" was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI - 2018). In this paper, we report the set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD). There were three principal sub-challenges: lesion segmentation, disease severity grading, and localization of retinal landmarks and segmentation. These multiple tasks in this challenge allow to test the generalizability of algorithms, and this is what makes it different from existing ones. It received a positive response from the scientific community with 148 submissions from 495 registrations effectively entered in this challenge. This paper outlines the challenge, its organization, the dataset used, evaluation methods and results of top-performing participating solutions. The top-performing approaches utilized a blend of clinical information, data augmentation, and an ensemble of models. These findings have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.
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Affiliation(s)
- Prasanna Porwal
- Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India; School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA.
| | - Samiksha Pachade
- Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India; School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA
| | - Manesh Kokare
- Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India
| | | | | | | | - Lihong Liu
- Ping An Technology (Shenzhen) Co.,Ltd, China
| | | | - Xinhui Liu
- Ping An Technology (Shenzhen) Co.,Ltd, China
| | | | - TianBo Wu
- Ping An Technology (Shenzhen) Co.,Ltd, China
| | - Jing Xiao
- Ping An Technology (Shenzhen) Co.,Ltd, China
| | | | | | - Yunzhi Wang
- School of Electrical and Computer Engineering, University of Oklahoma, USA
| | - Gopichandh Danala
- School of Electrical and Computer Engineering, University of Oklahoma, USA
| | - Linsheng He
- School of Electrical and Computer Engineering, University of Oklahoma, USA
| | - Yoon Ho Choi
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Yeong Chan Lee
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Sang-Hyuk Jung
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Zhongyu Li
- Department of Computer Science, University of North Carolina at Charlotte, USA
| | - Xiaodan Sui
- School of Information Science and Engineering, Shandong Normal University, China
| | - Junyan Wu
- Cleerly Inc., New York, United States
| | | | - Ting Zhou
- University at Buffalo, New York, United States
| | - Janos Toth
- University of Debrecen, Faculty of Informatics 4002 Debrecen, POB 400, Hungary
| | - Agnes Baran
- University of Debrecen, Faculty of Informatics 4002 Debrecen, POB 400, Hungary
| | | | | | | | | | - Xingzheng Lyu
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China; Machine Learning for Bioimage Analysis Group, Bioinformatics Institute, A*STAR, Singapore
| | - Li Cheng
- Machine Learning for Bioimage Analysis Group, Bioinformatics Institute, A*STAR, Singapore; Department of Electric and Computer Engineering, University of Alberta, Canada
| | - Qinhao Chu
- School of Computing, National University of Singapore, Singapore
| | - Pengcheng Li
- School of Computing, National University of Singapore, Singapore
| | - Xin Ji
- Beijing Shanggong Medical Technology Co., Ltd., China
| | - Sanyuan Zhang
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Yaxin Shen
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China
| | - Ling Dai
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China
| | | | | | - Tânia Melo
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
| | - Teresa Araújo
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal; FEUP - Faculty of Engineering of the University of Porto, Porto, Portugal
| | - Balazs Harangi
- University of Debrecen, Faculty of Informatics 4002 Debrecen, POB 400, Hungary
| | - Bin Sheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China
| | - Ruogu Fang
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, USA
| | | | - Andras Hajdu
- University of Debrecen, Faculty of Informatics 4002 Debrecen, POB 400, Hungary
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, China
| | - Ana Maria Mendonça
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal; FEUP - Faculty of Engineering of the University of Porto, Porto, Portugal
| | - Shaoting Zhang
- Department of Computer Science, University of North Carolina at Charlotte, USA
| | - Aurélio Campilho
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal; FEUP - Faculty of Engineering of the University of Porto, Porto, Portugal
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Luca Giancardo
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA
| | | | - Fabrice Mériaudeau
- Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Malaysia; ImViA/IFTIM, Université de Bourgogne, Dijon, France
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Liu Q, Hong X, Li S, Chen Z, Zhao G, Zou B. A spatial-aware joint optic disc and cup segmentation method. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.05.039] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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Dietter J, Haq W, Ivanov IV, Norrenberg LA, Völker M, Dynowski M, Röck D, Ziemssen F, Leitritz MA, Ueffing M. Optic disc detection in the presence of strong technical artifacts. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.04.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Gong C, Erichson NB, Kelly JP, Trutoiu L, Schowengerdt BT, Brunton SL, Seibel EJ. RetinaMatch: Efficient Template Matching of Retina Images for Teleophthalmology. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1993-2004. [PMID: 31217098 DOI: 10.1109/tmi.2019.2923466] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Retinal template matching and registration is an important challenge in teleophthalmology with low-cost imaging devices. However, the images from such devices generally have a small field of view (FOV) and image quality degradations, making matching difficult. In this paper, we develop an efficient and accurate retinal matching technique that combines dimension reduction and mutual information (MI), called RetinaMatch. The dimension reduction initializes the MI optimization as a coarse localization process, which narrows the optimization domain and avoids local optima. The effectiveness of RetinaMatch is demonstrated on the open fundus image database STARE with simulated reduced FOV and anticipated degradations, and on retinal images acquired by adapter-based optics attached to a smartphone. RetinaMatch achieves a success rate over 94% on human retinal images with the matched target registration errors below 2 pixels on average, excluding the observer variability, outperforming standard template matching solutions. In the application of measuring vessel diameter repeatedly, single pixel errors are expected. In addition, our method can be used in the process of image mosaicking with area-based registration, providing a robust approach when feature-based methods fail. To the best of our knowledge, this is the first template matching algorithm for retina images with small template images from unconstrained retinal areas. In the context of the emerging mixed reality market, we envision automated retinal image matching and registration methods as transformative for advanced teleophthalmology and long-term retinal monitoring.
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Bajwa MN, Malik MI, Siddiqui SA, Dengel A, Shafait F, Neumeier W, Ahmed S. Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning. BMC Med Inform Decis Mak 2019; 19:136. [PMID: 31315618 PMCID: PMC6637616 DOI: 10.1186/s12911-019-0842-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 06/19/2019] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND With the advancement of powerful image processing and machine learning techniques, Computer Aided Diagnosis has become ever more prevalent in all fields of medicine including ophthalmology. These methods continue to provide reliable and standardized large scale screening of various image modalities to assist clinicians in identifying diseases. Since optic disc is the most important part of retinal fundus image for glaucoma detection, this paper proposes a two-stage framework that first detects and localizes optic disc and then classifies it into healthy or glaucomatous. METHODS The first stage is based on Regions with Convolutional Neural Network (RCNN) and is responsible for localizing and extracting optic disc from a retinal fundus image while the second stage uses Deep Convolutional Neural Network to classify the extracted disc into healthy or glaucomatous. Unfortunately, none of the publicly available retinal fundus image datasets provides any bounding box ground truth required for disc localization. Therefore, in addition to the proposed solution, we also developed a rule-based semi-automatic ground truth generation method that provides necessary annotations for training RCNN based model for automated disc localization. RESULTS The proposed method is evaluated on seven publicly available datasets for disc localization and on ORIGA dataset, which is the largest publicly available dataset with healthy and glaucoma labels, for glaucoma classification. The results of automatic localization mark new state-of-the-art on six datasets with accuracy reaching 100% on four of them. For glaucoma classification we achieved Area Under the Receiver Operating Characteristic Curve equal to 0.874 which is 2.7% relative improvement over the state-of-the-art results previously obtained for classification on ORIGA dataset. CONCLUSION Once trained on carefully annotated data, Deep Learning based methods for optic disc detection and localization are not only robust, accurate and fully automated but also eliminates the need for dataset-dependent heuristic algorithms. Our empirical evaluation of glaucoma classification on ORIGA reveals that reporting only Area Under the Curve, for datasets with class imbalance and without pre-defined train and test splits, does not portray true picture of the classifier's performance and calls for additional performance metrics to substantiate the results.
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Affiliation(s)
- Muhammad Naseer Bajwa
- Fachbereich Informatik, Technische Universität Kaiserslautern, 67663 Kaiserslautern, Germany
- Deutsche Forschungszentrum für KünstlicheIntelligenz GmbH (DFKI), 67663 Kaiserslautern, Germany
| | - Muhammad Imran Malik
- Deep Learning Laboratory, National Center of Artificial Intelligence, Islamabad, 46000 Pakistan
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology, H-12, Islamabad, 46000 Pakistan
| | - Shoaib Ahmed Siddiqui
- Fachbereich Informatik, Technische Universität Kaiserslautern, 67663 Kaiserslautern, Germany
- Deutsche Forschungszentrum für KünstlicheIntelligenz GmbH (DFKI), 67663 Kaiserslautern, Germany
| | - Andreas Dengel
- Fachbereich Informatik, Technische Universität Kaiserslautern, 67663 Kaiserslautern, Germany
- Deutsche Forschungszentrum für KünstlicheIntelligenz GmbH (DFKI), 67663 Kaiserslautern, Germany
| | - Faisal Shafait
- Deep Learning Laboratory, National Center of Artificial Intelligence, Islamabad, 46000 Pakistan
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology, H-12, Islamabad, 46000 Pakistan
| | | | - Sheraz Ahmed
- Deutsche Forschungszentrum für KünstlicheIntelligenz GmbH (DFKI), 67663 Kaiserslautern, Germany
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Martinez-Perez ME, Witt N, Parker KH, Hughes AD, Thom SA. Automatic optic disc detection in colour fundus images by means of multispectral analysis and information content. PeerJ 2019; 7:e7119. [PMID: 31293825 PMCID: PMC6599671 DOI: 10.7717/peerj.7119] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 05/09/2019] [Indexed: 11/20/2022] Open
Abstract
The optic disc (OD) in retinal fundus images is widely used as a reference in computer-based systems for the measurement of the severity of retinal disease. A number of algorithms have been published in the past 5 years to locate and measure the OD in digital fundus images. Our proposed algorithm, automatically: (i) uses the three channels (RGB) of the digital colour image to locate the region of interest (ROI) where the OD lies, (ii) measures the Shannon information content per channel in the ROI, to decide which channel is most appropriate for searching for the OD centre using the circular Hough transform. A series of evaluations were undertaken to test our hypothesis that using the three channels gives a better performance than a single channel. Three different databases were used for evaluation purposes with a total of 2,371 colour images giving a misdetection error of 3% in the localisation of the centre of the OD. We find that the area determined by our algorithm which assumes that the OD is circular, is similar to that found by other algorithms that detected the shape of the OD. Five metrics were measured for comparison with other recent studies. Combining the two databases where expert delineation of the OD is available (1,240 images), the average results for our multispectral algorithm are: TPR = 0.879, FPR = 0.003, Accuracy = 0.994, Overlap = 80.6% and Dice index = 0.878.
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Affiliation(s)
- M. Elena Martinez-Perez
- Institute of Research on Applied Mathematics and Systems, Department of Computer Science, Universidad Nacional Autónoma de México, Mexico City, Mexico
- National Heart & Lung Institute, Imperial College, London, UK
| | - Nicholas Witt
- Department of Bioengineering, Imperial College, London, UK
| | - Kim H. Parker
- Department of Bioengineering, Imperial College, London, UK
| | - Alun D. Hughes
- National Heart & Lung Institute, Imperial College, London, UK
- Institute of Cardiovascular Sciences, University College London, London, UK
| | - Simon A.M. Thom
- National Heart & Lung Institute, Imperial College, London, UK
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Automatic Optic Disc Segmentation Based on Modified Local Image Fitting Model with Shape Prior Information. JOURNAL OF HEALTHCARE ENGINEERING 2019; 2019:2745183. [PMID: 31001406 PMCID: PMC6437741 DOI: 10.1155/2019/2745183] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 10/18/2018] [Accepted: 11/15/2018] [Indexed: 11/26/2022]
Abstract
Accurate optic disc (OD) detection is an essential yet vital step for retinal disease diagnosis. In the paper, an approach for segmenting OD boundary without manpower named full-automatic double boundary extraction is designed. There are two main advantages in it. (1) Since the performances and the computational cost produced by iterations of contour evolution of active contour models- (ACM-) based approaches greatly depend on the initialization, this paper proposes an effective and adaptive initial level set contour extraction approach using saliency detection and threshold techniques. (2) In order to handle unreliable information generated by intensity in abnormal retinal images caused by diseases, a modified LIF approach is presented by incorporating the shape prior information into LIF. We test the effectiveness of the proposed approach on a publicly available DIARETDB0 database. Experimental results demonstrate that our approach outperforms well-known approaches in terms of the average overlapping ratio and accuracy rate.
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23
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Automatic optic disc detection using low-rank representation based semi-supervised extreme learning machine. INT J MACH LEARN CYB 2019. [DOI: 10.1007/s13042-019-00939-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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24
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Automatic Optic Disc Detection in Color Retinal Images by Local Feature Spectrum Analysis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018; 2018:1942582. [PMID: 30013614 PMCID: PMC6022274 DOI: 10.1155/2018/1942582] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 05/15/2018] [Accepted: 05/23/2018] [Indexed: 11/17/2022]
Abstract
The optic disc is a key anatomical structure in retinal images. The ability to detect optic discs in retinal images plays an important role in automated screening systems. Inspired by the fact that humans can find optic discs in retinal images by observing some local features, we propose a local feature spectrum analysis (LFSA) that eliminates the influence caused by the variable spatial positions of local features. In LFSA, a dictionary of local features is used to reconstruct new optic disc candidate images, and the utilization frequencies of every atom in the dictionary are considered as a type of "spectrum" that can be used for classification. We also employ the sparse dictionary selection approach to construct a compact and representative dictionary. Unlike previous approaches, LFSA does not require the segmentation of vessels, and its method of considering the varying information in the retinal images is both simple and robust, making it well-suited for automated screening systems. Experimental results on the largest publicly available dataset indicate the effectiveness of our proposed approach.
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25
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Retinal Blood Vessel Segmentation by Using Matched Filtering and Fuzzy C-means Clustering with Integrated Level Set Method for Diabetic Retinopathy Assessment. J Med Biol Eng 2018. [DOI: 10.1007/s40846-018-0454-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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26
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Zhang J, Dashtbozorg B, Huang F, Tan T, ter Haar Romeny BM. A fully automated pipeline of extracting biomarkers to quantify vascular changes in retina-related diseases. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2018. [DOI: 10.1080/21681163.2018.1519851] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Jiong Zhang
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Behdad Dashtbozorg
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Fan Huang
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Tao Tan
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - B. M. ter Haar Romeny
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
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Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis. Symmetry (Basel) 2018. [DOI: 10.3390/sym10040087] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
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Bekkers EJ, Loog M, Romeny BMTH, Duits R. Template Matching via Densities on the Roto-Translation Group. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2018; 40:452-466. [PMID: 28252390 DOI: 10.1109/tpami.2017.2652452] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
We propose a template matching method for the detection of 2D image objects that are characterized by orientation patterns. Our method is based on data representations via orientation scores, which are functions on the space of positions and orientations, and which are obtained via a wavelet-type transform. This new representation allows us to detect orientation patterns in an intuitive and direct way, namely via cross-correlations. Additionally, we propose a generalized linear regression framework for the construction of suitable templates using smoothing splines. Here, it is important to recognize a curved geometry on the position-orientation domain, which we identify with the Lie group SE(2): the roto-translation group. Templates are then optimized in a B-spline basis, and smoothness is defined with respect to the curved geometry. We achieve state-of-the-art results on three different applications: detection of the optic nerve head in the retina (99.83 percent success rate on 1,737 images), of the fovea in the retina (99.32 percent success rate on 1,616 images), and of the pupil in regular camera images (95.86 percent on 1,521 images). The high performance is due to inclusion of both intensity and orientation features with effective geometric priors in the template matching. Moreover, our method is fast due to a cross-correlation based matching approach.
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30
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Retinal Vessels Segmentation Techniques and Algorithms: A Survey. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8020155] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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31
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Gui B, Shuai RJ, Chen P. Optic disc localization algorithm based on improved corner detection. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.procs.2018.04.169] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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32
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Li A, Niu Z, Cheng J, Yin F, Wong DWK, Yan S, Liu J. Learning supervised descent directions for optic disc segmentation. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.08.033] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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33
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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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Niu S, Chen Q, de Sisternes L, Leng T, Rubin DL. Automated detection of foveal center in SD-OCT images using the saliency of retinal thickness maps. Med Phys 2017; 44:6390-6403. [DOI: 10.1002/mp.12614] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2016] [Revised: 09/19/2017] [Accepted: 09/23/2017] [Indexed: 11/12/2022] Open
Affiliation(s)
- Sijie Niu
- School of Information Science and Engineering; University of Jinan; Jinan 250022 China
- School of Computer Science and Engineering; Nanjing University of Science and Technology; Nanjing 210094 China
| | - Qiang Chen
- School of Computer Science and Engineering; Nanjing University of Science and Technology; Nanjing 210094 China
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control; Minjiang University; Fuzhou 350121 China
| | | | - Theodore Leng
- Byers Eye Institute at Stanford; Stanford University School of Medicine; Palo Alto CA 94303 USA
| | - Daniel L. Rubin
- Department of Radiology; Stanford University; Stanford CA 94305 USA
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35
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Kumar JRH, Sachi S, Chaudhury K, Harsha S, Singh BK. A unified approach for detection of diagnostically significant regions-of-interest in retinal fundus images. TENCON 2017 - 2017 IEEE REGION 10 CONFERENCE 2017. [DOI: 10.1109/tencon.2017.8227829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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36
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Molina-Casado JM, Carmona EJ, García-Feijoó J. Fast detection of the main anatomical structures in digital retinal images based on intra- and inter-structure relational knowledge. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 149:55-68. [PMID: 28802330 DOI: 10.1016/j.cmpb.2017.06.022] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 06/15/2017] [Accepted: 06/23/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE The anatomical structure detection in retinal images is an open problem. However, most of the works in the related literature are oriented to the detection of each structure individually or assume the previous detection of a structure which is used as a reference. The objective of this paper is to obtain simultaneous detection of the main retinal structures (optic disc, macula, network of vessels and vascular bundle) in a fast and robust way. METHODS We propose a new methodology oriented to accomplish the mentioned objective. It consists of two stages. In an initial stage, a set of operators is applied to the retinal image. Each operator uses intra-structure relational knowledge in order to produce a set of candidate blobs that belongs to the desired structure. In a second stage, a set of tuples is created, each of which contains a different combination of the candidate blobs. Next, filtering operators, using inter-structure relational knowledge, are used in order to find the winner tuple. A method using template matching and mathematical morphology is implemented following the proposed methodology. RESULTS A success is achieved if the distance between the automatically detected blob center and the actual structure center is less than or equal to one optic disc radius. The success rates obtained in the different public databases analyzed were: MESSIDOR (99.33%, 98.58%, 97.92%), DIARETDB1 (96.63%, 100%, 97.75%), DRIONS (100%, n/a, 100%) and ONHSD (100%, 98.85%, 97.70%) for optic disc (OD), macula (M) and vascular bundle (VB), respectively. Finally, the overall success rate obtained in this study for each structure was: 99.26% (OD), 98.69% (M) and 98.95% (VB). The average time of processing per image was 4.16 ± 0.72 s. CONCLUSIONS The main advantage of the use of inter-structure relational knowledge was the reduction of the number of false positives in the detection process. The implemented method is able to simultaneously detect four structures. It is fast, robust and its detection results are competitive in relation to other methods of the recent literature.
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Affiliation(s)
- José M Molina-Casado
- Department of Artificial Intelligence, ETS Ingeniería Informática, Universidad Nacional de Educación a Distancia (UNED), C/ Juan del Rosal 16, Madrid 28040, Spain.
| | - Enrique J Carmona
- Department of Artificial Intelligence, ETS Ingeniería Informática, Universidad Nacional de Educación a Distancia (UNED), C/ Juan del Rosal 16, Madrid 28040, Spain.
| | - Julián García-Feijoó
- Department of Ophthalmology, Faculty of Medicine, Complutense University, Madrid, Spain; Ocular Pathology National Net OFTARED of the Institute of Health Carlos III, Spain; Department of Ophthalmology, Sanitary Research Institute of the San Carlos Clinical Hospital, Madrid, Spain.
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Sigut J, Nunez O, Fumero F, Gonzalez M, Arnay R. Contrast based circular approximation for accurate and robust optic disc segmentation in retinal images. PeerJ 2017; 5:e3763. [PMID: 28894642 PMCID: PMC5592085 DOI: 10.7717/peerj.3763] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 08/15/2017] [Indexed: 11/20/2022] Open
Abstract
A new method for automatic optic disc localization and segmentation is presented. The localization procedure combines vascular and brightness information to provide the best estimate of the optic disc center which is the starting point for the segmentation algorithm. A detection rate of 99.58% and 100% was achieved for the Messidor and ONHSD databases, respectively. A simple circular approximation to the optic disc boundary is proposed based on the maximum average contrast between the inner and outer ring of a circle centered on the estimated location. An average overlap coefficient of 0.890 and 0.865 was achieved for the same datasets, outperforming other state of the art methods. The results obtained confirm the advantages of using a simple circular model under non-ideal conditions as opposed to more complex deformable models.
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Affiliation(s)
- Jose Sigut
- Department of Computer Engineering and Systems, Universidad de La Laguna, San Cristobal de La Laguna, Spain
| | - Omar Nunez
- Department of Computer Engineering and Systems, Universidad de La Laguna, San Cristobal de La Laguna, Spain
| | - Francisco Fumero
- Department of Computer Engineering and Systems, Universidad de La Laguna, San Cristobal de La Laguna, Spain
| | - Marta Gonzalez
- Department of Ophthalmology, Hospital Universitario de Canarias, San Cristobal de La Laguna, Spain
| | - Rafael Arnay
- Department of Computer Engineering and Systems, Universidad de La Laguna, San Cristobal de La Laguna, Spain
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Chakravarty A, Sivaswamy J. Joint optic disc and cup boundary extraction from monocular fundus images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 147:51-61. [PMID: 28734530 DOI: 10.1016/j.cmpb.2017.06.004] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2017] [Revised: 05/04/2017] [Accepted: 06/20/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurate segmentation of optic disc and cup from monocular color fundus images plays a significant role in the screening and diagnosis of glaucoma. Though optic cup is characterized by the drop in depth from the disc boundary, most existing methods segment the two structures separately and rely only on color and vessel kink based cues due to the lack of explicit depth information in color fundus images. METHODS We propose a novel boundary-based Conditional Random Field formulation that extracts both the optic disc and cup boundaries in a single optimization step. In addition to the color gradients, the proposed method explicitly models the depth which is estimated from the fundus image itself using a coupled, sparse dictionary trained on a set of image-depth map (derived from Optical Coherence Tomography) pairs. RESULTS The estimated depth achieved a correlation coefficient of 0.80 with respect to the ground truth. The proposed segmentation method outperformed several state-of-the-art methods on five public datasets. The average dice coefficient was in the range of 0.87-0.97 for disc segmentation across three datasets and 0.83 for cup segmentation on the DRISHTI-GS1 test set. The method achieved a good glaucoma classification performance with an average AUC of 0.85 for five fold cross-validation on RIM-ONE v2. CONCLUSIONS We propose a method to jointly segment the optic disc and cup boundaries by modeling the drop in depth between the two structures. Since our method requires a single fundus image per eye during testing it can be employed in the large-scale screening of glaucoma where expensive 3D imaging is unavailable.
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Affiliation(s)
- Arunava Chakravarty
- Centre for Visual Information Technology, International Institute of Information Technology Hyderabad, 500032, India.
| | - Jayanthi Sivaswamy
- Centre for Visual Information Technology, International Institute of Information Technology Hyderabad, 500032, India.
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39
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Rodrigues LC, Marengoni M. Segmentation of optic disc and blood vessels in retinal images using wavelets, mathematical morphology and Hessian-based multi-scale filtering. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2017.03.014] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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40
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Kamble R, Kokare M, Deshmukh G, Hussin FA, Mériaudeau F. Localization of optic disc and fovea in retinal images using intensity based line scanning analysis. Comput Biol Med 2017; 87:382-396. [PMID: 28595892 DOI: 10.1016/j.compbiomed.2017.04.016] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Revised: 04/25/2017] [Accepted: 04/25/2017] [Indexed: 11/15/2022]
Abstract
Accurate detection of diabetic retinopathy (DR) mainly depends on identification of retinal landmarks such as optic disc and fovea. Present methods suffer from challenges like less accuracy and high computational complexity. To address this issue, this paper presents a novel approach for fast and accurate localization of optic disc (OD) and fovea using one-dimensional scanned intensity profile analysis. The proposed method utilizes both time and frequency domain information effectively for localization of OD. The final OD center is located using signal peak-valley detection in time domain and discontinuity detection in frequency domain analysis. However, with the help of detected OD location, the fovea center is located using signal valley analysis. Experiments were conducted on MESSIDOR dataset, where OD was successfully located in 1197 out of 1200 images (99.75%) and fovea in 1196 out of 1200 images (99.66%) with an average computation time of 0.52s. The large scale evaluation has been carried out extensively on nine publicly available databases. The proposed method is highly efficient in terms of quickly and accurately localizing OD and fovea structure together compared with the other state-of-the-art methods.
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Affiliation(s)
- Ravi Kamble
- SGGS Institute of Engineering and Technology, Nanded, MS, India.
| | - Manesh Kokare
- SGGS Institute of Engineering and Technology, Nanded, MS, India
| | | | - Fawnizu Azmadi Hussin
- Department of Electrical and Electronic Engineering, Center for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi Petronas, Tronoh, 32610 Seri Iskandar, Perak, Malaysia
| | - Fabrice Mériaudeau
- Department of Electrical and Electronic Engineering, Center for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi Petronas, Tronoh, 32610 Seri Iskandar, Perak, Malaysia
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41
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Zilly J, Buhmann JM, Mahapatra D. Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation. Comput Med Imaging Graph 2017; 55:28-41. [PMID: 27590198 DOI: 10.1016/j.compmedimag.2016.07.012] [Citation(s) in RCA: 116] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Revised: 07/16/2016] [Accepted: 07/29/2016] [Indexed: 11/18/2022]
Affiliation(s)
- Julian Zilly
- Department of Mechanical Engineering, ETH Zurich, Switzerland
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42
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Echegaray S, Nair V, Kadoch M, Leung A, Rubin D, Gevaert O, Napel S. A Rapid Segmentation-Insensitive "Digital Biopsy" Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non-Small Cell Lung Cancer. ACTA ACUST UNITED AC 2016; 2:283-294. [PMID: 28612050 PMCID: PMC5466872 DOI: 10.18383/j.tom.2016.00163] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Quantitative imaging approaches compute features within images' regions of interest. Segmentation is rarely completely automatic, requiring time-consuming editing by experts. We propose a new paradigm, called “digital biopsy,” that allows for the collection of intensity- and texture-based features from these regions at least 1 order of magnitude faster than the current manual or semiautomated methods. A radiologist reviewed automated segmentations of lung nodules from 100 preoperative volume computed tomography scans of patients with non–small cell lung cancer, and manually adjusted the nodule boundaries in each section, to be used as a reference standard, requiring up to 45 minutes per nodule. We also asked a different expert to generate a digital biopsy for each patient using a paintbrush tool to paint a contiguous region of each tumor over multiple cross-sections, a procedure that required an average of <3 minutes per nodule. We simulated additional digital biopsies using morphological procedures. Finally, we compared the features extracted from these digital biopsies with our reference standard using intraclass correlation coefficient (ICC) to characterize robustness. Comparing the reference standard segmentations to our digital biopsies, we found that 84/94 features had an ICC >0.7; comparing erosions and dilations, using a sphere of 1.5-mm radius, of our digital biopsies to the reference standard segmentations resulted in 41/94 and 53/94 features, respectively, with ICCs >0.7. We conclude that many intensity- and texture-based features remain consistent between the reference standard and our method while substantially reducing the amount of operator time required.
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Affiliation(s)
- Sebastian Echegaray
- Department of Electrical Engineering, Stanford University, Stanford, California
| | - Viswam Nair
- Department of Radiology, Stanford University School of Medicine, Stanford, California.,Department of Medicine, Stanford University School of Medicine, Stanford, California.,Canary Center for Cancer Early Detection, Stanford University, Stanford, California
| | - Michael Kadoch
- Department of Radiology, Stanford University School of Medicine, Stanford, California
| | - Ann Leung
- Department of Radiology, Stanford University School of Medicine, Stanford, California
| | - Daniel Rubin
- Department of Radiology, Stanford University School of Medicine, Stanford, California.,Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - Olivier Gevaert
- Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - Sandy Napel
- Department of Radiology, Stanford University School of Medicine, Stanford, California
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Besenczi R, Tóth J, Hajdu A. A review on automatic analysis techniques for color fundus photographs. Comput Struct Biotechnol J 2016; 14:371-384. [PMID: 27800125 PMCID: PMC5072151 DOI: 10.1016/j.csbj.2016.10.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 10/01/2016] [Accepted: 10/03/2016] [Indexed: 12/25/2022] Open
Abstract
In this paper, we give a review on automatic image processing tools to recognize diseases causing specific distortions in the human retina. After a brief summary of the biology of the retina, we give an overview of the types of lesions that may appear as biomarkers of both eye and non-eye diseases. We present several state-of-the-art procedures to extract the anatomic components and lesions in color fundus photographs and decision support methods to help clinical diagnosis. We list publicly available databases and appropriate measurement techniques to compare quantitatively the performance of these approaches. Furthermore, we discuss on how the performance of image processing-based systems can be improved by fusing the output of individual detector algorithms. Retinal image analysis using mobile phones is also addressed as an expected future trend in this field.
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Key Words
- ACC, accuracy
- AMD, age-related macular degeneration
- AUC, area under the receiver operator characteristics curve
- Biomedical imaging
- Clinical decision support
- DR, diabetic retinopathy
- FN, false negative
- FOV, field-of-view
- FP, false positive
- FPI, false positive per image
- Fundus image analysis
- MA, microaneurysm
- NA, not available
- OC, optic cup
- OD, optic disc
- PPV, positive predictive value (precision)
- ROC, Retinopathy Online Challenge
- RS, Retinopathy Online Challenge score
- Retinal diseases
- SCC, Spearman's rank correlation coefficient
- SE, sensitivity
- SP, specificity
- TN, true negative
- TP, true positive
- kNN, k-nearest neighbor
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Affiliation(s)
- Renátó Besenczi
- Faculty of Informatics, University of Debrecen 4002 Debrecen PO Box 400, Hungary
| | - János Tóth
- Faculty of Informatics, University of Debrecen 4002 Debrecen PO Box 400, Hungary
| | - András Hajdu
- Faculty of Informatics, University of Debrecen 4002 Debrecen PO Box 400, Hungary
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Singh A, Dutta MK, Sharma DK. Unique identification code for medical fundus images using blood vessel pattern for tele-ophthalmology applications. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 135:61-75. [PMID: 27586480 DOI: 10.1016/j.cmpb.2016.07.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2015] [Revised: 06/03/2016] [Accepted: 07/01/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE Identification of fundus images during transmission and storage in database for tele-ophthalmology applications is an important issue in modern era. The proposed work presents a novel accurate method for generation of unique identification code for identification of fundus images for tele-ophthalmology applications and storage in databases. Unlike existing methods of steganography and watermarking, this method does not tamper the medical image as nothing is embedded in this approach and there is no loss of medical information. METHODS Strategic combination of unique blood vessel pattern and patient ID is considered for generation of unique identification code for the digital fundus images. Segmented blood vessel pattern near the optic disc is strategically combined with patient ID for generation of a unique identification code for the image. RESULTS The proposed method of medical image identification is tested on the publically available DRIVE and MESSIDOR database of fundus image and results are encouraging. CONCLUSIONS Experimental results indicate the uniqueness of identification code and lossless recovery of patient identity from unique identification code for integrity verification of fundus images.
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Affiliation(s)
- Anushikha Singh
- Department of Electronics & Communication Engineering, Amity University, Noida, India
| | - Malay Kishore Dutta
- Department of Electronics & Communication Engineering, Amity University, Noida, India.
| | - Dilip Kumar Sharma
- Department of Computer Engineering & Applications, GLA University, Mathura, India
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Wu X, Dai B, Bu W. Optic Disc Localization Using Directional Models. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2016; 25:4433-4442. [PMID: 27416600 DOI: 10.1109/tip.2016.2590838] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Reliable localization of the optic disc (OD) is important for retinal image analysis and ophthalmic pathology screening. This paper presents a novel method to automatically localize ODs in retinal fundus images based on directional models. According to the characteristics of retina vessel networks, such as their origin at the OD and parabolic shape of the main vessels, a global directional model, named the relaxed biparabola directional model, is first built. In this model, the main vessels are modeled by using two parabolas with a shared vertex and different parameters. Then, a local directional model, named the disc directional model, is built to characterize the local vessel convergence in the OD as well as the shape and the brightness of the OD. Finally, the global and the local directional models are integrated to form a hybrid directional model, which can exploit the advantages of the global and local models for highly accurate OD localization. The proposed method is evaluated on nine publicly available databases, and achieves an accuracy of 100% for each database, which demonstrates the effectiveness of the proposed OD localization method.
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Amini N, Miraftabi A, Henry S, Chung N, Nowroozizadeh S, Caprioli J, Nouri-Mahdavi K. The Relationship of the Clinical Disc Margin and Bruch's Membrane Opening in Normal and Glaucoma Subjects. Invest Ophthalmol Vis Sci 2016; 57:1468-75. [PMID: 27031840 PMCID: PMC4819565 DOI: 10.1167/iovs.15-18382] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Purpose We tested the hypotheses that the mismatch between the clinical disc margin (CDM) and Bruch's membrane opening (BMO) is a function of BMO area (BMOA) and is affected by the presence of glaucoma. Methods A total of 45 normal eyes (45 subjects) and 53 glaucomatous eyes (53 patients) were enrolled and underwent radial optic nerve head (ONH) imaging with spectral domain optical coherence tomography. The inner tip of the Bruch's membrane (BM) and the clinical disc margin were marked on radial scans and optic disc photographs, and were coregistered with custom software. The main outcome measure was the difference between the clinical disc area (CDA) and BMOA, or CDA−BMOA mismatch, as a function of BMOA and diagnosis. Multivariate regression analyses were used to explore the influence of glaucoma and BMOA on the mismatch. Results Global CDA was larger than BMOA in both groups but the difference was statistically significant only in the normal group (1.98 ± 0.37 vs. 1.85 ± 0.45 mm2, P = 0.02 in the normal group; 1.96 ± 0.38 vs. 1.89 ± 0.56 mm2, P = 0.08 in the glaucoma group). The sectoral CDA−BMOA mismatch was smaller in superotemporal (P = 0.04) and superonasal (P = 0.05) sectors in the glaucoma group. The normalized CDA−BMOA difference decreased with increasing BMOA in both groups (P < 0.001). Presence or severity of glaucoma did not affect the CDA−BMOA difference (P > 0.14). Conclusions Clinical disc area was larger than BMOA in normal and glaucoma eyes but reached statistical significance only in the former group. The CDA−BMOA mismatch diminished with increasing BMOA but was not affected by presence of glaucoma. These findings have important clinical implications regarding clinical evaluation of the ONH.
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Wang L, Liu G, Fu S, Xu L, Zhao K, Zhang C. Retinal Image Enhancement Using Robust Inverse Diffusion Equation and Self-Similarity Filtering. PLoS One 2016; 11:e0158480. [PMID: 27388503 PMCID: PMC4936706 DOI: 10.1371/journal.pone.0158480] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Accepted: 06/17/2016] [Indexed: 11/18/2022] Open
Abstract
As a common ocular complication for diabetic patients, diabetic retinopathy has become an important public health problem in the world. Early diagnosis and early treatment with the help of fundus imaging technology is an effective control method. In this paper, a robust inverse diffusion equation combining a self-similarity filtering is presented to detect and evaluate diabetic retinopathy using retinal image enhancement. A flux corrected transport technique is used to control diffusion flux adaptively, which eliminates overshoots inherent in the Laplacian operation. Feature preserving denoising by the self-similarity filtering ensures a robust enhancement of noisy and blurry retinal images. Experimental results demonstrate that this algorithm can enhance important details of retinal image data effectively, affording an opportunity for better medical interpretation and subsequent processing.
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Affiliation(s)
- Lu Wang
- School of Public Health, Shandong University, Jinan 250012, China
| | - Guohua Liu
- Department of Ophthalmology, Qilu Children’s Hospital of Shandong University, Jinan 250022, China
| | - Shujun Fu
- School of Mathematics, Shandong University, Jinan 250100, China
| | - Lingzhong Xu
- School of Public Health, Shandong University, Jinan 250012, China
| | - Kun Zhao
- Department of Medical Imaging, The Second Hospital of Shandong University, Jinan 250033, China
| | - Caiming Zhang
- School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250061, China
- School of Computer Science and Technology, Shandong University, Jinan 250101, China
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48
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Automatic Optic Disc and Fovea Detection in Retinal Images Using Super-Elliptical Convergence Index Filters. ACTA ACUST UNITED AC 2016. [DOI: 10.1007/978-3-319-41501-7_78] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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49
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Ren F, Li W, Yang J, Geng H, Zhao D. Automatic optic disc localization and segmentation in retinal images by a line operator and level sets. Technol Health Care 2016; 24 Suppl 2:S767-76. [DOI: 10.3233/thc-161206] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Fulong Ren
- School of Information Science & Engineering, Northeastern University, Shenyang, Liaoning, China
- Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Wei Li
- Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Jinzhu Yang
- School of Information Science & Engineering, Northeastern University, Shenyang, Liaoning, China
- Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Huan Geng
- School of Information Science & Engineering, Northeastern University, Shenyang, Liaoning, China
- Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Dazhe Zhao
- School of Information Science & Engineering, Northeastern University, Shenyang, Liaoning, China
- Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, Liaoning, China
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Abdullah M, Fraz MM, Barman SA. Localization and segmentation of optic disc in retinal images using circular Hough transform and grow-cut algorithm. PeerJ 2016; 4:e2003. [PMID: 27190713 PMCID: PMC4867714 DOI: 10.7717/peerj.2003] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2016] [Accepted: 04/12/2016] [Indexed: 11/20/2022] Open
Abstract
Automated retinal image analysis has been emerging as an important diagnostic tool for early detection of eye-related diseases such as glaucoma and diabetic retinopathy. In this paper, we have presented a robust methodology for optic disc detection and boundary segmentation, which can be seen as the preliminary step in the development of a computer-assisted diagnostic system for glaucoma in retinal images. The proposed method is based on morphological operations, the circular Hough transform and the grow-cut algorithm. The morphological operators are used to enhance the optic disc and remove the retinal vasculature and other pathologies. The optic disc center is approximated using the circular Hough transform, and the grow-cut algorithm is employed to precisely segment the optic disc boundary. The method is quantitatively evaluated on five publicly available retinal image databases DRIVE, DIARETDB1, CHASE_DB1, DRIONS-DB, Messidor and one local Shifa Hospital Database. The method achieves an optic disc detection success rate of 100% for these databases with the exception of 99.09% and 99.25% for the DRIONS-DB, Messidor, and ONHSD databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 78.6%, 85.12%, 83.23%, 85.1%, 87.93%, 80.1%, and 86.1%, respectively, for these databases. This unique method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc.
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Affiliation(s)
- Muhammad Abdullah
- School of Electrical Engineering and Computer Science, National University of Sciences and Technology , Islamabad , Pakistan
| | - Muhammad Moazam Fraz
- School of Electrical Engineering and Computer Science, National University of Sciences and Technology , Islamabad , Pakistan
| | - Sarah A Barman
- Faculty of Science Engineering and Computing, Kingston University , London , United Kingdom
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