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Nakayama LF, Zago Ribeiro L, Novaes F, Miyawaki IA, Miyawaki AE, de Oliveira JAE, Oliveira T, Malerbi FK, Regatieri CVS, Celi LA, Silva PS. Artificial intelligence for telemedicine diabetic retinopathy screening: a review. Ann Med 2023; 55:2258149. [PMID: 37734417 PMCID: PMC10515659 DOI: 10.1080/07853890.2023.2258149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 08/31/2023] [Indexed: 09/23/2023] Open
Abstract
PURPOSE This study aims to compare artificial intelligence (AI) systems applied in diabetic retinopathy (DR) teleophthalmology screening, currently deployed systems, fairness initiatives and the challenges for implementation. METHODS The review included articles retrieved from PubMed/Medline/EMBASE literature search strategy regarding telemedicine, DR and AI. The screening criteria included human articles in English, Portuguese or Spanish and related to telemedicine and AI for DR screening. The author's affiliations and the study's population income group were classified according to the World Bank Country and Lending Groups. RESULTS The literature search yielded a total of 132 articles, and nine were included after full-text assessment. The selected articles were published between 2004 and 2020 and were grouped as telemedicine systems, algorithms, economic analysis and image quality assessment. Four telemedicine systems that perform a quality assessment, image preprocessing and pathological screening were reviewed. A data and post-deployment bias assessment are not performed in any of the algorithms, and none of the studies evaluate the social impact implementations. There is a lack of representativeness in the reviewed articles, with most authors and target populations from high-income countries and no low-income country representation. CONCLUSIONS Telemedicine and AI hold great promise for augmenting decision-making in medical care, expanding patient access and enhancing cost-effectiveness. Economic studies and social science analysis are crucial to support the implementation of AI in teleophthalmology screening programs. Promoting fairness and generalizability in automated systems combined with telemedicine screening programs is not straightforward. Improving data representativeness, reducing biases and promoting equity in deployment and post-deployment studies are all critical steps in model development.
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Affiliation(s)
- Luis Filipe Nakayama
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Ophthalmology, São Paulo Federal University, Sao Paulo, Brazil
| | - Lucas Zago Ribeiro
- Department of Ophthalmology, São Paulo Federal University, Sao Paulo, Brazil
| | - Frederico Novaes
- Department of Ophthalmology, São Paulo Federal University, Sao Paulo, Brazil
| | | | | | | | - Talita Oliveira
- Department of Ophthalmology, São Paulo Federal University, Sao Paulo, Brazil
| | | | | | - Leo Anthony Celi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Paolo S. Silva
- Beetham Eye Institute, Joslin Diabetes Centre, Harvard Medical School, Boston, MA, USA
- Philippine Eye Research Institute, University of the Philippines, Manila, Philippines
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Biswas S, Khan MIA, Hossain MT, Biswas A, Nakai T, Rohdin J. Which Color Channel Is Better for Diagnosing Retinal Diseases Automatically in Color Fundus Photographs? LIFE (BASEL, SWITZERLAND) 2022; 12:life12070973. [PMID: 35888063 PMCID: PMC9321111 DOI: 10.3390/life12070973] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 05/25/2022] [Accepted: 06/01/2022] [Indexed: 11/22/2022]
Abstract
Color fundus photographs are the most common type of image used for automatic diagnosis of retinal diseases and abnormalities. As all color photographs, these images contain information about three primary colors, i.e., red, green, and blue, in three separate color channels. This work aims to understand the impact of each channel in the automatic diagnosis of retinal diseases and abnormalities. To this end, the existing works are surveyed extensively to explore which color channel is used most commonly for automatically detecting four leading causes of blindness and one retinal abnormality along with segmenting three retinal landmarks. From this survey, it is clear that all channels together are typically used for neural network-based systems, whereas for non-neural network-based systems, the green channel is most commonly used. However, from the previous works, no conclusion can be drawn regarding the importance of the different channels. Therefore, systematic experiments are conducted to analyse this. A well-known U-shaped deep neural network (U-Net) is used to investigate which color channel is best for segmenting one retinal abnormality and three retinal landmarks.
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Affiliation(s)
- Sangeeta Biswas
- Faculty of Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.I.A.K.); (M.T.H.)
- Correspondence: or
| | - Md. Iqbal Aziz Khan
- Faculty of Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.I.A.K.); (M.T.H.)
| | - Md. Tanvir Hossain
- Faculty of Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.I.A.K.); (M.T.H.)
| | - Angkan Biswas
- CAPM Company Limited, Bonani, Dhaka 1213, Bangladesh;
| | - Takayoshi Nakai
- Faculty of Engineering, Shizuoka University, Hamamatsu 432-8561, Japan;
| | - Johan Rohdin
- Faculty of Information Technology, Brno University of Technology, 61200 Brno, Czech Republic;
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Das D, Biswas SK, Bandyopadhyay S. A critical review on diagnosis of diabetic retinopathy using machine learning and deep learning. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:25613-25655. [PMID: 35342328 PMCID: PMC8940593 DOI: 10.1007/s11042-022-12642-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 06/29/2021] [Accepted: 02/09/2022] [Indexed: 06/12/2023]
Abstract
Diabetic Retinopathy (DR) is a health condition caused due to Diabetes Mellitus (DM). It causes vision problems and blindness due to disfigurement of human retina. According to statistics, 80% of diabetes patients battling from long diabetic period of 15 to 20 years, suffer from DR. Hence, it has become a dangerous threat to the health and life of people. To overcome DR, manual diagnosis of the disease is feasible but overwhelming and cumbersome at the same time and hence requires a revolutionary method. Thus, such a health condition necessitates primary recognition and diagnosis to prevent DR from developing into severe stages and prevent blindness. Innumerable Machine Learning (ML) models are proposed by researchers across the globe, to achieve this purpose. Various feature extraction techniques are proposed for extraction of DR features for early detection. However, traditional ML models have shown either meagre generalization throughout feature extraction and classification for deploying smaller datasets or consumes more of training time causing inefficiency in prediction while using larger datasets. Hence Deep Learning (DL), a new domain of ML, is introduced. DL models can handle a smaller dataset with help of efficient data processing techniques. However, they generally incorporate larger datasets for their deep architectures to enhance performance in feature extraction and image classification. This paper gives a detailed review on DR, its features, causes, ML models, state-of-the-art DL models, challenges, comparisons and future directions, for early detection of DR.
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Affiliation(s)
- Dolly Das
- National Institute of Technology Silchar, Cachar, Assam India
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Barros DMS, Moura JCC, Freire CR, Taleb AC, Valentim RAM, Morais PSG. Machine learning applied to retinal image processing for glaucoma detection: review and perspective. Biomed Eng Online 2020; 19:20. [PMID: 32293466 PMCID: PMC7160894 DOI: 10.1186/s12938-020-00767-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 04/06/2020] [Indexed: 02/07/2023] Open
Abstract
INTRODUCTION This is a systematic review on the main algorithms using machine learning (ML) in retinal image processing for glaucoma diagnosis and detection. ML has proven to be a significant tool for the development of computer aided technology. Furthermore, secondary research has been widely conducted over the years for ophthalmologists. Such aspects indicate the importance of ML in the context of retinal image processing. METHODS The publications that were chosen to compose this review were gathered from Scopus, PubMed, IEEEXplore and Science Direct databases. Then, the papers published between 2014 and 2019 were selected . Researches that used the segmented optic disc method were excluded. Moreover, only the methods which applied the classification process were considered. The systematic analysis was performed in such studies and, thereupon, the results were summarized. DISCUSSION Based on architectures used for ML in retinal image processing, some studies applied feature extraction and dimensionality reduction to detect and isolate important parts of the analyzed image. Differently, other works utilized a deep convolutional network. Based on the evaluated researches, the main difference between the architectures is the number of images demanded for processing and the high computational cost required to use deep learning techniques. CONCLUSIONS All the analyzed publications indicated it was possible to develop an automated system for glaucoma diagnosis. The disease severity and its high occurrence rates justify the researches which have been carried out. Recent computational techniques, such as deep learning, have shown to be promising technologies in fundus imaging. Although such a technique requires an extensive database and high computational costs, the studies show that the data augmentation and transfer learning techniques have been applied as an alternative way to optimize and reduce networks training.
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Affiliation(s)
- Daniele M S Barros
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil.
| | - Julio C C Moura
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Cefas R Freire
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil
| | | | - Ricardo A M Valentim
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Philippi S G Morais
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil
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Guo X, Wang H, Lu X, Hu X, Che S, Lu Y. Robust Fovea Localization Based on Symmetry Measure. IEEE J Biomed Health Inform 2020; 24:2315-2326. [PMID: 32031956 DOI: 10.1109/jbhi.2020.2971593] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Automatic fovea localization is a challenging issue. In this article, we focus on the study of fovea localization and propose a robust fovea localization method. We propose concentric circular sectional symmetry measure (CCSSM) for symmetry axis detection, and region of interest (ROI) determination, which is a global feature descriptor robust against local feature changes, to solve the lesion interference issue, i.e., fovea visibility interference from lesions, using both structure features and morphological features. We propose the index of convexity and concavity (ICC) as the convexity-concavity measure of the surface and provide a quantitative evaluation tool for ophthalmologists to learn whether the occurrence of lesion within the ROI. We propose the weighted gradient accumulation map, which is insensitive to local intensity changes and can overcome the influence of noise and contamination, to perform refined localization. The advantages of the proposed method lies in two aspects. First, the accuracy and robustness can be achieved without typical sophisticated manner, i.e., blood vessel segmentation and parabola fitting. Second, the lesion interference is considered in our plan of fovea localization. Our proposed symmetry-based method is innovative in the solution of fovea detection, and it is simple, practical, and controllable. Experiment results show that the proposed method can resist the interference of unbalanced illumination and lesions, and achieve high accuracy rate in five datasets. Compared to the state-of-the-art methods, high robustness and accuracy of the proposed method guarantees its reliability.
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Yan Q, Zhao Y, Zheng Y, Liu Y, Zhou K, Frangi A, Liu J. Automated retinal lesion detection via image saliency analysis. Med Phys 2019; 46:4531-4544. [PMID: 31381173 DOI: 10.1002/mp.13746] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 07/11/2019] [Accepted: 07/22/2019] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND AND OBJECTIVE The detection of abnormalities such as lesions or leakage from retinal images is an important health informatics task for automated early diagnosis of diabetic and malarial retinopathy or other eye diseases, in order to prevent blindness and common systematic conditions. In this work, we propose a novel retinal lesion detection method by adapting the concepts of saliency. METHODS Retinal images are first segmented as superpixels, two new saliency feature representations: uniqueness and compactness, are then derived to represent the superpixels. The pixel level saliency is then estimated from these superpixel saliency values via a bilateral filter. These extracted saliency features form a matrix for low-rank analysis to achieve saliency detection. The precise contour of a lesion is finally extracted from the generated saliency map after removing confounding structures such as blood vessels, the optic disk, and the fovea. The main novelty of this method is that it is an effective tool for detecting different abnormalities at the pixel level from different modalities of retinal images, without the need to tune parameters. RESULTS To evaluate its effectiveness, we have applied our method to seven public datasets of diabetic and malarial retinopathy with four different types of lesions: exudate, hemorrhage, microaneurysms, and leakage. The evaluation was undertaken at the pixel level, lesion level, or image level according to ground truth availability in these datasets. CONCLUSIONS The experimental results show that the proposed method outperforms existing state-of-the-art ones in applicability, effectiveness, and accuracy.
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Affiliation(s)
- Qifeng Yan
- University of Chinese Academy of Sciences, Beijing, 100049, China.,Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Cixi, 315399, China
| | - Yitian Zhao
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Cixi, 315399, China
| | - Yalin Zheng
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Cixi, 315399, China.,Department of Eye and Vision Science, University of Liverpool, Liverpool, L7 8TX, UK
| | - Yonghuai Liu
- Department of Computer Science, Edge Hill University, Ormskirk, L39 4QP, UK
| | - Kang Zhou
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Cixi, 315399, China.,School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Alejandro Frangi
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Cixi, 315399, China.,School of Computing, University of Leeds, Leeds, S2 9JT, UK
| | - Jiang Liu
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Cixi, 315399, China.,Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
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A model of ganglion axon pathways accounts for percepts elicited by retinal implants. Sci Rep 2019; 9:9199. [PMID: 31235711 PMCID: PMC6591412 DOI: 10.1038/s41598-019-45416-4] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 06/04/2019] [Indexed: 11/09/2022] Open
Abstract
Degenerative retinal diseases such as retinitis pigmentosa and macular degeneration cause irreversible vision loss in more than 10 million people worldwide. Retinal prostheses, now implanted in over 250 patients worldwide, electrically stimulate surviving cells in order to evoke neuronal responses that are interpreted by the brain as visual percepts ('phosphenes'). However, instead of seeing focal spots of light, current implant users perceive highly distorted phosphenes that vary in shape both across subjects and electrodes. We characterized these distortions by asking users of the Argus retinal prosthesis system (Second Sight Medical Products Inc.) to draw electrically elicited percepts on a touchscreen. Using ophthalmic fundus imaging and computational modeling, we show that elicited percepts can be accurately predicted by the topographic organization of optic nerve fiber bundles in each subject's retina, successfully replicating visual percepts ranging from 'blobs' to oriented 'streaks' and 'wedges' depending on the retinal location of the stimulating electrode. This provides the first evidence that activation of passing axon fibers accounts for the rich repertoire of phosphene shape commonly reported in psychophysical experiments, which can severely distort the quality of the generated visual experience. Overall our findings argue for more detailed modeling of biological detail across neural engineering applications.
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8
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Chalakkal RJ, Abdulla WH, Thulaseedharan SS. Quality and content analysis of fundus images using deep learning. Comput Biol Med 2019; 108:317-331. [DOI: 10.1016/j.compbiomed.2019.03.019] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 03/20/2019] [Accepted: 03/21/2019] [Indexed: 11/28/2022]
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Optic Disc Localization in Complicated Environment of Retinal Image Using Circular-Like Estimation. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2019. [DOI: 10.1007/s13369-019-03756-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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10
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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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Diabetic retinopathy techniques in retinal images: A review. Artif Intell Med 2018; 97:168-188. [PMID: 30448367 DOI: 10.1016/j.artmed.2018.10.009] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 10/08/2018] [Accepted: 10/24/2018] [Indexed: 12/23/2022]
Abstract
The diabetic retinopathy is the main reason of vision loss in people. Medical experts recognize some clinical, geometrical and haemodynamic features of diabetic retinopathy. These features include the blood vessel area, exudates, microaneurysm, hemorrhages and neovascularization, etc. In Computer Aided Diagnosis (CAD) systems, these features are detected in fundus images using computer vision techniques. In this paper, we review the methods of low, middle and high level vision for automatic detection and classification of diabetic retinopathy.We give a detailed review of 79 algorithms for detecting different features of diabetic retinopathy during the last eight years.
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Saba T, Bokhari STF, Sharif M, Yasmin M, Raza M. Fundus image classification methods for the detection of glaucoma: A review. Microsc Res Tech 2018; 81:1105-1121. [DOI: 10.1002/jemt.23094] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 06/07/2018] [Accepted: 06/19/2018] [Indexed: 01/31/2023]
Affiliation(s)
- Tanzila Saba
- College of Computer and Information SciencesPrince Sultan University Riyadh Saudi Arabia
| | | | - Muhammad Sharif
- Department of Computer ScienceCOMSATS University Islamabad Wah Campus Pakistan
| | - Mussarat Yasmin
- Department of Computer ScienceCOMSATS University Islamabad Wah Campus Pakistan
| | - Mudassar Raza
- Department of Computer ScienceCOMSATS University Islamabad Wah Campus Pakistan
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13
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Automatic detection of optic disc in color fundus retinal images using circle operator. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.05.027] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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14
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Vignarajan J, Kanagasingam Y. Retinal hemorrhage detection by rule-based and machine learning approach. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2017:660-663. [PMID: 29059959 DOI: 10.1109/embc.2017.8036911] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Robust detection of hemorrhages (HMs) in color fundus image is important in an automatic diabetic retinopathy grading system. Detection of the hemorrhages that are close to or connected with retinal blood vessels was found to be challenge. However, most methods didn't put research on it, even some of them mentioned this issue. In this paper, we proposed a novel hemorrhage detection method based on rule-based and machine learning methods. We focused on the improvement of detection of the hemorrhages that are close to or connected with retinal blood vessels, besides detecting the independent hemorrhage regions. A preliminary test for detecting HM presence was conducted on the images from two databases. We achieved sensitivity and specificity of 93.3% and 88% as well as 91.9% and 85.6% on the two datasets.
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Punniyamoorthy U, Pushpam I. Remote examination of exudates-impact of macular oedema. Healthc Technol Lett 2018; 5:118-123. [PMID: 30155263 PMCID: PMC6103783 DOI: 10.1049/htl.2017.0026] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Revised: 03/15/2018] [Accepted: 04/05/2018] [Indexed: 11/20/2022] Open
Abstract
One of the major causes of eye blindness is identified to be as diabetic retinopathy, which if not detected in earlier stage would cause a serious issue. Long-term diabetes causes diabetic retinopathy. The significant key factor leading to diabetic retinopathy is exudates which affect the retina part and causes eye defects. Thus the first and foremost task in the automated detection of macular oedema is to detect the presence of these exudates. The authors use image processing techniques to detect the optic disc, exudates and the presence of macular oedema. Their method has the sensitivity 96.07%, selectivity 97.36%, and accuracy 96.62% for the exudates detection and in the case of macular oedema detection the sensitivity 97.75%, selectivity 100%, and accuracy 98.86% is achieved. The performance comparison with other methods reveals that their method can be used as a screening process for diabetic retinopathy. In addition to that, the algorithm can help to detect macular oedema.
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Affiliation(s)
- Uma Punniyamoorthy
- Department of Electronics, Madras Institute of Technology, Anna University Campus, Chennai, Tamilnadu 600044, India
| | - Indumathi Pushpam
- Department of Electronics, Madras Institute of Technology, Anna University Campus, Chennai, Tamilnadu 600044, India
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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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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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Murugeswari S, Sukanesh R. Investigations of severity level measurements for diabetic macular oedema using machine learning algorithms. Ir J Med Sci 2017; 186:929-938. [PMID: 28508191 DOI: 10.1007/s11845-017-1598-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Accepted: 03/24/2017] [Indexed: 12/01/2022]
Abstract
BACKGROUND The macula is an important part of the human visual system and is responsible for clear and colour vision. Macular oedema happens when fluid and protein deposit on or below the macula of the eye and cause the macula to thicken and swell. Normally, it occurs due to diabetes called diabetic macular oedema. Diabetic macular oedema (DME) is one of the main causes of visual impairment in patients. AIM The aims of the present study are to detect and localize abnormalities in blood vessels with respect to macula in order to prevent vision loss for the diabetic patients. METHODS In this work, a novel fully computerized algorithm is used for the recognition of various diseases in macula using both fundus images and optical coherence tomography (OCT) images. Abnormal blood vessels are segmented using thresholding algorithm. The classification is performed by three different classifiers, namely, the support vector machine (SVM), cascade neural network (CNN) and partial least square (PLS) classifiers, which are employed to identify whether the image is normal or abnormal. CONCLUSION The results of all of the classifiers are compared based on their accuracy. The classifier accuracies of the SVM, cascade neural network and partial least square are 98.33, 97.16 and 94.34%, respectively. While analysing DME using both images, OCT produced efficient output than fundus images. Information about the severity of the disease and the localization of the pathologies is very useful to the ophthalmologist for diagnosing disease and choosing the proper treatment for a patient to prevent vision loss.
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Affiliation(s)
- S Murugeswari
- Syed Ammal Engineering College, Ramanathapuram, Tamil Nadu, India.
| | - R Sukanesh
- Thiagarajar College of Engineering, Madurai, India
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Multiscale segmentation of exudates in retinal images using contextual cues and ensemble classification. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2017.02.012] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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20
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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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Prentašić P, Lončarić S. Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 137:281-292. [PMID: 28110732 DOI: 10.1016/j.cmpb.2016.09.018] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 09/14/2016] [Accepted: 09/22/2016] [Indexed: 05/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Diabetic retinopathy is one of the leading disabling chronic diseases and one of the leading causes of preventable blindness in developed world. Early diagnosis of diabetic retinopathy enables timely treatment and in order to achieve it a major effort will have to be invested into automated population screening programs. Detection of exudates in color fundus photographs is very important for early diagnosis of diabetic retinopathy. METHODS We use deep convolutional neural networks for exudate detection. In order to incorporate high level anatomical knowledge about potential exudate locations, output of the convolutional neural network is combined with the output of the optic disc detection and vessel detection procedures. RESULTS In the validation step using a manually segmented image database we obtain a maximum F1 measure of 0.78. CONCLUSIONS As manually segmenting and counting exudate areas is a tedious task, having a reliable automated output, such as automated segmentation using convolutional neural networks in combination with other landmark detectors, is an important step in creating automated screening programs for early detection of diabetic retinopathy.
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Affiliation(s)
- Pavle Prentašić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia.
| | - Sven Lončarić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia
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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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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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Onal S, Chen X, Satamraju V, Balasooriya M, Dabil-Karacal H. Automated and simultaneous fovea center localization and macula segmentation using the new dynamic identification and classification of edges model. J Med Imaging (Bellingham) 2016; 3:034002. [PMID: 27660803 DOI: 10.1117/1.jmi.3.3.034002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Accepted: 08/23/2016] [Indexed: 11/14/2022] Open
Abstract
Detecting the position of retinal structures, including the fovea center and macula, in retinal images plays a key role in diagnosing eye diseases such as optic nerve hypoplasia, amblyopia, diabetic retinopathy, and macular edema. However, current detection methods are unreliable for infants or certain ethnic populations. Thus, a methodology is proposed here that may be useful for infants and across ethnicities that automatically localizes the fovea center and segments the macula on digital fundus images. First, dark structures and bright artifacts are removed from the input image using preprocessing operations, and the resulting image is transformed to polar space. Second, the fovea center is identified, and the macula region is segmented using the proposed dynamic identification and classification of edges (DICE) model. The performance of the method was evaluated using 1200 fundus images obtained from the relatively large, diverse, and publicly available Messidor database. In 96.1% of these 1200 cases, the distance between the fovea center identified manually by ophthalmologists and automatically using the proposed method remained within 0 to 8 pixels. The dice similarity index comparing the manually obtained results with those of the model for macula segmentation was 96.12% for these 1200 cases. Thus, the proposed method displayed a high degree of accuracy. The methodology using the DICE model is unique and advantageous over previously reported methods because it simultaneously determines the fovea center and segments the macula region without using any structural information, such as optic disc or blood vessel location, and it may prove useful for all populations, including infants.
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Affiliation(s)
- Sinan Onal
- Southern Illinois University-Edwardsville , Department of Mechanical and Industrial Engineering, Box 1805, Edwardsville, Illinois 62026, United States
| | - Xin Chen
- Southern Illinois University-Edwardsville , Department of Mechanical and Industrial Engineering, Box 1805, Edwardsville, Illinois 62026, United States
| | - Veeresh Satamraju
- Southern Illinois University-Edwardsville , Department of Electrical and Computer, Box 1801, Edwardsville, Illinois 62026, United States
| | - Maduka Balasooriya
- Southern Illinois University-Edwardsville , Department of Mathematics and Statistics, Box 1805, Edwardsville, Illinois 62026, United States
| | - Humeyra Dabil-Karacal
- Washington University , School of Medicine, Department of Ophthalmology and Visual Sciences, 10 Barnes West Drive Suite 201 C, St. Louis, Missouri 63141, United States
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Xiong L, Li H. An approach to locate optic disc in retinal images with pathological changes. Comput Med Imaging Graph 2016; 47:40-50. [DOI: 10.1016/j.compmedimag.2015.10.003] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2015] [Revised: 10/15/2015] [Accepted: 10/27/2015] [Indexed: 11/26/2022]
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26
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Application of different imaging modalities for diagnosis of Diabetic Macular Edema: A review. Comput Biol Med 2015; 66:295-315. [PMID: 26453760 DOI: 10.1016/j.compbiomed.2015.09.012] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2015] [Revised: 09/10/2015] [Accepted: 09/14/2015] [Indexed: 11/23/2022]
Abstract
Diabetic Macular Edema (DME) is caused by accumulation of extracellular fluid from hyperpermeable capillaries within the macula. DME is one of the leading causes of blindness among Diabetes Mellitus (DM) patients. Early detection followed by laser photocoagulation can save the visual loss. This review discusses various imaging modalities viz. biomicroscopy, Fluorescein Angiography (FA), Optical Coherence Tomography (OCT) and colour fundus photographs used for diagnosis of DME. Various automated DME grading systems using retinal fundus images, associated retinal image processing techniques for fovea, exudate detection and segmentation are presented. We have also compared various imaging modalities and automated screening methods used for DME grading. The reviewed literature indicates that FA and OCT identify DME related changes accurately. FA is an invasive method, which uses fluorescein dye, and OCT is an expensive imaging method compared to fundus photographs. Moreover, using fundus images DME can be identified and automated. DME grading algorithms can be implemented for telescreening. Hence, fundus imaging based DME grading is more suitable and affordable method compared to biomicroscopy, FA, and OCT modalities.
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Rahebi J, Hardalaç F. A new approach to optic disc detection in human retinal images using the firefly algorithm. Med Biol Eng Comput 2015; 54:453-61. [PMID: 26093773 DOI: 10.1007/s11517-015-1330-7] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2014] [Accepted: 06/08/2015] [Indexed: 12/01/2022]
Abstract
There are various methods and algorithms to detect the optic discs in retinal images. In recent years, much attention has been given to the utilization of the intelligent algorithms. In this paper, we present a new automated method of optic disc detection in human retinal images using the firefly algorithm. The firefly intelligent algorithm is an emerging intelligent algorithm that was inspired by the social behavior of fireflies. The population in this algorithm includes the fireflies, each of which has a specific rate of lighting or fitness. In this method, the insects are compared two by two, and the less attractive insects can be observed to move toward the more attractive insects. Finally, one of the insects is selected as the most attractive, and this insect presents the optimum response to the problem in question. Here, we used the light intensity of the pixels of the retinal image pixels instead of firefly lightings. The movement of these insects due to local fluctuations produces different light intensity values in the images. Because the optic disc is the brightest area in the retinal images, all of the insects move toward brightest area and thus specify the location of the optic disc in the image. The results of implementation show that proposed algorithm could acquire an accuracy rate of 100 % in DRIVE dataset, 95 % in STARE dataset, and 94.38 % in DiaRetDB1 dataset. The results of implementation reveal high capability and accuracy of proposed algorithm in the detection of the optic disc from retinal images. Also, recorded required time for the detection of the optic disc in these images is 2.13 s for DRIVE dataset, 2.81 s for STARE dataset, and 3.52 s for DiaRetDB1 dataset accordingly. These time values are average value.
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Affiliation(s)
- Javad Rahebi
- Department of Electrical and Electronics Engineering, Gazi University, Ankara, Turkey.
| | - Fırat Hardalaç
- Department of Electrical and Electronics Engineering, Gazi University, Ankara, Turkey
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Sim DA, Keane PA, Tufail A, Egan CA, Aiello LP, Silva PS. Automated retinal image analysis for diabetic retinopathy in telemedicine. Curr Diab Rep 2015; 15:14. [PMID: 25697773 DOI: 10.1007/s11892-015-0577-6] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
There will be an estimated 552 million persons with diabetes globally by the year 2030. Over half of these individuals will develop diabetic retinopathy, representing a nearly insurmountable burden for providing diabetes eye care. Telemedicine programmes have the capability to distribute quality eye care to virtually any location and address the lack of access to ophthalmic services. In most programmes, there is currently a heavy reliance on specially trained retinal image graders, a resource in short supply worldwide. These factors necessitate an image grading automation process to increase the speed of retinal image evaluation while maintaining accuracy and cost effectiveness. Several automatic retinal image analysis systems designed for use in telemedicine have recently become commercially available. Such systems have the potential to substantially improve the manner by which diabetes eye care is delivered by providing automated real-time evaluation to expedite diagnosis and referral if required. Furthermore, integration with electronic medical records may allow a more accurate prognostication for individual patients and may provide predictive modelling of medical risk factors based on broad population data.
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Affiliation(s)
- Dawn A Sim
- Department of Ophthalmology, Harvard Medical School and Beetham Eye Institute, Joslin Diabetes Center, One Joslin Place, Boston, MA, 02215, USA
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Garduno-Alvarado T, Martinez-Perez ME, Martinez-Castellanos MA, Rodriguez-Quinones L, Salinas-Longoria SM. Optic disc and macula detection in fundus images by means of template matching. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:134-7. [PMID: 25569915 DOI: 10.1109/embc.2014.6943547] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Various methods for detecting optic disc and macula in fundus images have been developed. Our aim is to propose a fairly easy method for detecting both features jointly. This is achieved by first correcting in homogenous luminosity using a polynomial approximation of the background of the images. Secondly, the use of the cross-correlation in the frequency domain between the images and a steerable template which contains both structures. The 38 photographs used in this work belong to a local database of patients suffering diabetic retinopathy along its four severity stages. Our results showed 100% optic disc centers located within the OD area and 90% macula centers located within the MC area.
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Santhi D, Manimegalai D, Karkuzhali S. DIAGNOSIS OF DIABETIC RETINOPATHY BY EXUDATES DETECTION USING CLUSTERING TECHNIQUES. BIOMEDICAL ENGINEERING: APPLICATIONS, BASIS AND COMMUNICATIONS 2014. [DOI: 10.4015/s101623721450077x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Diabetes is the most prevalent disease that affects the retina and leads to blindness without any symptoms. An adverse change in retinal blood vessels that leads to vision loss is called as Diabetic Retinopathy (DR). DR is one among the leading causes of blindness worldwide. There is an increasing interest to design the medical system for screening and diagnosis of DR. Segmentation of exudates is essential for diagnostic purpose. In this regard, Optic Disc (OD) center is detected by template matching technique and then it is masked to avoid misclassification in the results of exudates detection. In this paper, we proposed a novel K-Means nearest neighbor algorithm, combining K-means with morphology and Fuzzy to segment exudates. The main advantage of the proposed approach is that it does not depend upon manually selected parameters. Performances of these algorithms are compared with existing algorithms like Fuzzy C means (FCM) and Spatially Weighted Fuzzy C Means (SWFCM). These different segmentation algorithms are applied to publically available STARE data set and it is found that mean sensitivity, specificity and accuracy values for the fuzzy algorithm is 91%, 94% and 93% respectively and considerably higher than other algorithms.
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Affiliation(s)
- D. Santhi
- Department of Electronics and Instrumentation Engineering, National Engineering College, Kovilpatti, Tamilnadu, India
| | - D. Manimegalai
- Department of Information Technology, National Engineering College, Kovilpatti, Tamilnadu, India
| | - S. Karkuzhali
- Department of Information Technology, National Engineering College, Kovilpatti, Tamilnadu, India
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Aquino A. Establishing the macular grading grid by means of fovea centre detection using anatomical-based and visual-based features. Comput Biol Med 2014; 55:61-73. [DOI: 10.1016/j.compbiomed.2014.10.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2014] [Revised: 10/06/2014] [Accepted: 10/10/2014] [Indexed: 11/29/2022]
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Kao EF, Lin PC, Chou MC, Jaw TS, Liu GC. Automated detection of fovea in fundus images based on vessel-free zone and adaptive Gaussian template. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 117:92-103. [PMID: 25168776 DOI: 10.1016/j.cmpb.2014.08.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2014] [Revised: 08/06/2014] [Accepted: 08/06/2014] [Indexed: 05/11/2023]
Abstract
This study developed a computerised method for fovea centre detection in fundus images. In the method, the centre of the optic disc was localised first by the template matching method, the disc-fovea axis (a line connecting the optic disc centre and the fovea) was then determined by searching the vessel-free region, and finally the fovea centre was detected by matching the fovea template around the centre of the axis. Adaptive Gaussian templates were used to localise the centres of the optic disc and fovea for the images with different resolutions. The proposed method was evaluated using three publicly available databases (DIARETDB0, DIARETDB1 and MESSIDOR), which consisted of a total of 1419 fundus images with different resolutions. The proposed method obtained the fovea detection accuracies of 93.1%, 92.1% and 97.8% for the DIARETDB0, DIARETDB1 and MESSIDOR databases, respectively. The overall accuracy of the proposed method was 97.0% in this study.
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Affiliation(s)
- E-Fong Kao
- Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan.
| | - Pi-Chen Lin
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Ming-Chung Chou
- Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Twei-Shiun Jaw
- Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Gin-Chung Liu
- Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
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Zhang X, Thibault G, Decencière E, Marcotegui B, Laÿ B, Danno R, Cazuguel G, Quellec G, Lamard M, Massin P, Chabouis A, Victor Z, Erginay A. Exudate detection in color retinal images for mass screening of diabetic retinopathy. Med Image Anal 2014; 18:1026-43. [PMID: 24972380 DOI: 10.1016/j.media.2014.05.004] [Citation(s) in RCA: 183] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2013] [Revised: 04/22/2014] [Accepted: 05/07/2014] [Indexed: 11/16/2022]
Affiliation(s)
- Xiwei Zhang
- Centre for Mathematical Morphology, Mathematics and Systems Department, MINES ParisTech, 35 rue Saint-Honoré, Fontainebleau, France.
| | - Guillaume Thibault
- Centre for Mathematical Morphology, Mathematics and Systems Department, MINES ParisTech, 35 rue Saint-Honoré, Fontainebleau, France
| | - Etienne Decencière
- Centre for Mathematical Morphology, Mathematics and Systems Department, MINES ParisTech, 35 rue Saint-Honoré, Fontainebleau, France.
| | - Beatriz Marcotegui
- Centre for Mathematical Morphology, Mathematics and Systems Department, MINES ParisTech, 35 rue Saint-Honoré, Fontainebleau, France
| | - Bruno Laÿ
- ADCIS, 3 rue Martin Luther King, 14280 Saint-Contest, France
| | - Ronan Danno
- ADCIS, 3 rue Martin Luther King, 14280 Saint-Contest, France
| | - Guy Cazuguel
- Télécom Bretagne, Institut Mines-Télécom, ITI Department, Brest, France; Inserm UMR 1101 LaTIM U650, bâtiment I, CHRU Morvan, 29200 Brest, France
| | - Gwénolé Quellec
- Inserm UMR 1101 LaTIM U650, bâtiment I, CHRU Morvan, 29200 Brest, France
| | - Mathieu Lamard
- Télécom Bretagne, Institut Mines-Télécom, ITI Department, Brest, France; Inserm UMR 1101 LaTIM U650, bâtiment I, CHRU Morvan, 29200 Brest, France
| | - Pascale Massin
- Service d'ophtalmologie, hôpital Lariboisière, APHP, 2, rue Ambroise-Paré, 75475 Paris cedex 10, France
| | - Agnès Chabouis
- Direction de la politique médicale, parcours des patients et organisations médicales innovantes télémédecine, Assistance publique Hôpitaux de Paris, 3, avenue Victoria, 75184 Paris cedex 04, France
| | - Zeynep Victor
- Service d'ophtalmologie, hôpital Lariboisière, APHP, 2, rue Ambroise-Paré, 75475 Paris cedex 10, France
| | - Ali Erginay
- Service d'ophtalmologie, hôpital Lariboisière, APHP, 2, rue Ambroise-Paré, 75475 Paris cedex 10, France
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Ganesan K, Acharya RU, Chua CK, Laude A. Identification and localization of fovea on colour fundus images using blur scales. Proc Inst Mech Eng H 2014; 228:962-70. [DOI: 10.1177/0954411914550847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Identification of retinal landmarks is an important step in the extraction of anomalies in retinal fundus images. In the current study, we propose a technique to identify and localize the position of macula and hence the fovea avascular zone, in colour fundus images. The proposed method, based on varying blur scales in images, is independent of the location of other anatomical landmarks present in the fundus images. Experimental results have been provided using the open database MESSIDOR by validating our segmented regions using the dice coefficient, with ground truth segmentation provided by a human expert. Apart from testing the images on the entire MESSIDOR database, the proposed technique was also validated using 50 normal and 50 diabetic retinopathy chosen digital fundus images from the same database. A maximum overlap accuracy of 89.6%−93.8% and locational accuracy of 94.7%−98.9% was obtained for identification and localization of the fovea.
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Affiliation(s)
- Karthikeyan Ganesan
- Department of Electronic and Computer Engineering (ECE), Ngee Ann Polytechnic, Singapore
| | - Rajendra U Acharya
- Department of Electronic and Computer Engineering (ECE), Ngee Ann Polytechnic, Singapore
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Chua Kuang Chua
- Department of Electronic and Computer Engineering (ECE), Ngee Ann Polytechnic, Singapore
| | - Augustinus Laude
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore
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A Fovea Localization Scheme Using Vessel Origin-Based Parabolic Model. ALGORITHMS 2014. [DOI: 10.3390/a7030456] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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36
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Zheng Y, Daniel E, Hunter AA, Xiao R, Gao J, Li H, Maguire MG, Brainard DH, Gee JC. Landmark matching based retinal image alignment by enforcing sparsity in correspondence matrix. Med Image Anal 2014; 18:903-13. [PMID: 24238743 PMCID: PMC4141885 DOI: 10.1016/j.media.2013.09.009] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2013] [Revised: 09/06/2013] [Accepted: 09/23/2013] [Indexed: 11/21/2022]
Abstract
Retinal image alignment is fundamental to many applications in diagnosis of eye diseases. In this paper, we address the problem of landmark matching based retinal image alignment. We propose a novel landmark matching formulation by enforcing sparsity in the correspondence matrix and offer its solutions based on linear programming. The proposed formulation not only enables a joint estimation of the landmark correspondences and a predefined transformation model but also combines the benefits of the softassign strategy (Chui and Rangarajan, 2003) and the combinatorial optimization of linear programming. We also introduced a set of reinforced self-similarities descriptors which can better characterize local photometric and geometric properties of the retinal image. Theoretical analysis and experimental results with both fundus color images and angiogram images show the superior performances of our algorithms to several state-of-the-art techniques.
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Affiliation(s)
- Yuanjie Zheng
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
| | - Ebenezer Daniel
- Department of Ophthalmology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Allan A Hunter
- Department of Ophthalmology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Rui Xiao
- Department of Biostatistics and Epidemiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Jianbin Gao
- University of Electronic Science and Technology, Chengdu, Sichuan, China
| | - Hongsheng Li
- University of Electronic Science and Technology, Chengdu, Sichuan, China
| | - Maureen G Maguire
- Department of Ophthalmology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - David H Brainard
- Department of Psychology, School of Arts and Sciences at the University of Pennsylvania, Philadelphia, PA, USA
| | - James C Gee
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
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37
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Pourreza-Shahri R, Tavakoli M, Kehtarnavaz N. Computationally efficient optic nerve head detection in retinal fundus images. Biomed Signal Process Control 2014. [DOI: 10.1016/j.bspc.2014.02.011] [Citation(s) in RCA: 28] [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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38
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Akram MU, Tariq A, Khan SA, Javed MY. Automated detection of exudates and macula for grading of diabetic macular edema. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 114:141-152. [PMID: 24548898 DOI: 10.1016/j.cmpb.2014.01.010] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2013] [Revised: 11/17/2013] [Accepted: 01/08/2014] [Indexed: 06/03/2023]
Abstract
Medical systems based on state of the art image processing and pattern recognition techniques are very common now a day. These systems are of prime interest to provide basic health care facilities to patients and support to doctors. Diabetic macular edema is one of the retinal abnormalities in which diabetic patient suffers from severe vision loss due to affected macula. It affects the central vision of the person and causes total blindness in severe cases. In this article, we propose an intelligent system for detection and grading of macular edema to assist the ophthalmologists in early and automated detection of the disease. The proposed system consists of a novel method for accurate detection of macula using a detailed feature set and Gaussian mixtures model based classifier. We also present a new hybrid classifier as an ensemble of Gaussian mixture model and support vector machine for improved exudate detection even in the presence of other bright lesions which eventually leads to reliable classification of input retinal image in different stages of macular edema. The statistical analysis and comparative evaluation of proposed system with existing methods are performed on publicly available standard retinal image databases. The proposed system has achieved average value of 97.3%, 95.9% and 96.8% for sensitivity, specificity and accuracy respectively on both databases.
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Affiliation(s)
- M Usman Akram
- Department of Computer Engineering College of E&ME, National University of Sciences and Technology, Peshawar Road, Rawalpindi, Pakistan.
| | - Anam Tariq
- Department of Computer Engineering College of E&ME, National University of Sciences and Technology, Peshawar Road, Rawalpindi, Pakistan.
| | - Shoab A Khan
- Department of Computer Engineering College of E&ME, National University of Sciences and Technology, Peshawar Road, Rawalpindi, Pakistan.
| | - M Younus Javed
- Department of Computer Engineering College of E&ME, National University of Sciences and Technology, Peshawar Road, Rawalpindi, Pakistan.
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39
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Figueiredo IN, Kumar S. Automatic Optic Disc Detection in Retinal Fundus Images Based on Geometric Features. LECTURE NOTES IN COMPUTER SCIENCE 2014. [DOI: 10.1007/978-3-319-11755-3_32] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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40
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Mookiah MRK, Acharya UR, Chua CK, Lim CM, Ng EYK, Laude A. Computer-aided diagnosis of diabetic retinopathy: a review. Comput Biol Med 2013; 43:2136-55. [PMID: 24290931 DOI: 10.1016/j.compbiomed.2013.10.007] [Citation(s) in RCA: 168] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2013] [Revised: 09/27/2013] [Accepted: 10/04/2013] [Indexed: 11/29/2022]
Abstract
Diabetes mellitus may cause alterations in the retinal microvasculature leading to diabetic retinopathy. Unchecked, advanced diabetic retinopathy may lead to blindness. It can be tedious and time consuming to decipher subtle morphological changes in optic disk, microaneurysms, hemorrhage, blood vessels, macula, and exudates through manual inspection of fundus images. A computer aided diagnosis system can significantly reduce the burden on the ophthalmologists and may alleviate the inter and intra observer variability. This review discusses the available methods of various retinal feature extractions and automated analysis.
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41
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Karnowski TP, Giancardo L, Li Y, Tobin KW, Chaum E. Retina image analysis and ocular telehealth: the Oak Ridge National Laboratory-Hamilton Eye Institute case study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:7140-3. [PMID: 24111391 DOI: 10.1109/embc.2013.6611204] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Automated retina image analysis has reached a high level of maturity in recent years, and thus the question of how validation is performed in these systems is beginning to grow in importance. One application of retina image analysis is in telemedicine, where an automated system could enable the automated detection of diabetic retinopathy and other eye diseases as a low-cost method for broad-based screening. In this work, we discuss our experiences in developing a telemedical network for retina image analysis, including our progression from a manual diagnosis network to a more fully automated one. We pay special attention to how validations of our algorithm steps are performed, both using data from the telemedicine network and other public databases.
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42
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Li B, Li HK. Automated analysis of diabetic retinopathy images: principles, recent developments, and emerging trends. Curr Diab Rep 2013; 13:453-9. [PMID: 23686810 DOI: 10.1007/s11892-013-0393-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Diabetic retinopathy (DR) is a vision-threatening complication of diabetes. Timely diagnosis and intervention are essential for treatment that reduces the risk of vision loss. A good color retinal (fundus) photograph can be used as a surrogate for face-to-face evaluation of DR. The use of computers to assist or fully automate DR evaluation from retinal images has been studied for many years. Early work showed promising results for algorithms in detecting and classifying DR pathology. Newer techniques include those that adapt machine learning technology to DR image analysis. Challenges remain, however, that must be overcome before fully automatic DR detection and analysis systems become practical clinical tools.
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Affiliation(s)
- Baoxin Li
- School of Computing, Informatics & Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, USA.
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43
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Gegundez-Arias ME, Marin D, Bravo JM, Suero A. Locating the fovea center position in digital fundus images using thresholding and feature extraction techniques. Comput Med Imaging Graph 2013; 37:386-93. [DOI: 10.1016/j.compmedimag.2013.06.002] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2013] [Accepted: 06/12/2013] [Indexed: 10/26/2022]
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44
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Chin KS, Trucco E, Tan L, Wilson PJ. Automatic fovea location in retinal images using anatomical priors and vessel density. Pattern Recognit Lett 2013. [DOI: 10.1016/j.patrec.2013.03.016] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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45
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Welfer D, Scharcanski J, Marinho DR. A morphologic two-stage approach for automated optic disk detection in color eye fundus images. Pattern Recognit Lett 2013. [DOI: 10.1016/j.patrec.2012.12.011] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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46
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Effect of streptozotocin-induced diabetes mellitus on the cerebellar cortex of adult male albino rats. ACTA ACUST UNITED AC 2013. [DOI: 10.1097/01.ehx.0000424090.98199.b8] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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47
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Wong DWK, Liu J, Tan NM, Yin F, Cheng X, Cheng CY, Cheung GCM, Wong TY. Automatic detection of the macula in retinal fundus images using seeded mode tracking approach. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:4950-3. [PMID: 23367038 DOI: 10.1109/embc.2012.6347103] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The macula is the part of the eye responsible for central high acuity vision. Detection of the macula is an important task in retinal image processing as a landmark for subsequent disease assessment, such as for age-related macula degeneration. In this paper, we have presented an approach to automatically determine the macula centre in retinal fundus images. First contextual information on the image is combined with a statistical model to obtain an approximate macula region of interest localization. Subsequently, we propose the use of a seeded mode tracking technique to locate the macula centre. The proposed approach is tested on a large dataset composed of 482 normal images and 162 glaucoma images from the ORIGA database and an additional 96 AMD images. The results show a ROI detection of 97.5%, and 90.5% correct detection of the macula within 1/3DD from a manual reference, which outperforms other current methods. The results are promising for the use of the proposed approach to locate the macula for the detection of macula diseases from retinal images.
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48
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Abràmoff M, Kay CN. Image Processing. Retina 2013. [DOI: 10.1016/b978-1-4557-0737-9.00006-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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49
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Devisetti K, Karnowski TP, Giancardo L, Li Y, Chaum E. Geographic atrophy segmentation in infrared and autofluorescent retina images using supervised learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:3958-61. [PMID: 22255206 DOI: 10.1109/iembs.2011.6090983] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Geographic Atrophy (GA) of the retinal pigment epithelium (RPE) is an advanced form of atrophic age-related macular degeneration (AMD) and is responsible for about 20% of AMD-related legal blindness in the United States. Two different imaging modalities for retinas, infrared imaging and autofluorescence imaging, serve as interesting complimentary technologies for highlighting GA. In this work we explore the use of neural network classifiers in performing segmentation of GA in registered infrared (IR) and autofluorescence (AF) images. Our segmentation achieved a performance level of 82.5% sensitivity and 92.9% specificity on a per-pixel basis using hold-one-out validation testing. The algorithm, feature extraction, data set and experimental results are discussed and shown.
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Affiliation(s)
- K Devisetti
- University of Tennessee Health Science Center, 930 Madison Avenue, Suite 731, Memphis, TN 38163, USA
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50
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Detecting optic disc on asians by multiscale gaussian filtering. Int J Biomed Imaging 2012; 2012:727154. [PMID: 22844267 PMCID: PMC3392893 DOI: 10.1155/2012/727154] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2012] [Revised: 04/23/2012] [Accepted: 04/23/2012] [Indexed: 11/18/2022] Open
Abstract
The optic disc (OD) is an important anatomical feature in retinal images, and its detection is vital for developing automated screening programs. Currently, there is no algorithm designed to automatically detect the OD in fundus images captured from Asians which are larger and have thicker vessels compared to Caucasians. In this paper, we propose such a method to complement current algorithms using two steps: OD vessel candidate detection and OD vessel candidate matching. The first step is achieved with multiscale Gaussian filtering, scale production, and double thresholding to initially extract the vessels' directional map of various thicknesses. The map is then thinned before another threshold is applied to remove pixels with low intensities. This result forms the OD vessel candidates. In the second step, a Vessels' Directional Matched Filter (VDMF) of various dimensions is applied to the candidates to be matched, and the pixel with the smallest difference designated the OD center. We tested the proposed method on a new database consisting of 402 images from a diabetic retinopathy (DR) screening programme consisting of Asians. The OD center was successfully detected with an accuracy of 99.25% (399/402).
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