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Bidwai P, Gite S, Gupta A, Pahuja K, Kotecha K. Multimodal dataset using OCTA and fundus images for the study of diabetic retinopathy. Data Brief 2024; 52:110033. [PMID: 38299103 PMCID: PMC10828556 DOI: 10.1016/j.dib.2024.110033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/03/2024] [Accepted: 01/03/2024] [Indexed: 02/02/2024] Open
Abstract
This article presents a Multimodal database consisting of 222 images of 76 people wherein 111 are OCTA images and 111 are color fundus images taken at the Natasha Eye Care and Research Institute of Pune Maharashtra, India. Nonmydriatic fundus images were acquired using a confocal SLO widefield fundus imaging Eidon machine. Nonmydriatic OCTA images were acquired using the Optovue Avanti Edition machine Initially, the clinical approach described in this article was used to obtain the retinal images. Following that, the dataset was categorized by two experienced eye specialists. To identify instances of Non-Proliferative Diabetic Retinopathy (NPDR) with their various stages, medical professionals and scholars can use this data. Research scholars and ophthalmologists can utilize the data created to develop the initial stages of automated identification techniques for diabetic retinopathy (DR).
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Affiliation(s)
- Pooja Bidwai
- Symbiosis Centre for Applied Artificial Intelligence (SCAAI) Symbiosis Institute of Technology, Symbiosis International (Deemed University) (SIU), Lavale, Pune 412115, India
| | - Shilpa Gite
- Symbiosis Centre for Applied Artificial Intelligence (SCAAI) Symbiosis Institute of Technology, Symbiosis International (Deemed University) (SIU), Lavale, Pune 412115, India
| | - Aditi Gupta
- Natasha Eye Care Shiv Sai Lane Pimple Saudagar, Pune, Maharashtra 411027, India
| | - Kishore Pahuja
- Natasha Eye Care Shiv Sai Lane Pimple Saudagar, Pune, Maharashtra 411027, India
| | - Ketan Kotecha
- Symbiosis Institute of Technology, Symbiosis International (Deemed University) (SIU), Lavale, Pune 412115, India
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2
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Shoukat A, Akbar S, Hassan SA, Iqbal S, Mehmood A, Ilyas QM. Automatic Diagnosis of Glaucoma from Retinal Images Using Deep Learning Approach. Diagnostics (Basel) 2023; 13:diagnostics13101738. [PMID: 37238222 DOI: 10.3390/diagnostics13101738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 05/04/2023] [Accepted: 05/06/2023] [Indexed: 05/28/2023] Open
Abstract
Glaucoma is characterized by increased intraocular pressure and damage to the optic nerve, which may result in irreversible blindness. The drastic effects of this disease can be avoided if it is detected at an early stage. However, the condition is frequently detected at an advanced stage in the elderly population. Therefore, early-stage detection may save patients from irreversible vision loss. The manual assessment of glaucoma by ophthalmologists includes various skill-oriented, costly, and time-consuming methods. Several techniques are in experimental stages to detect early-stage glaucoma, but a definite diagnostic technique remains elusive. We present an automatic method based on deep learning that can detect early-stage glaucoma with very high accuracy. The detection technique involves the identification of patterns from the retinal images that are often overlooked by clinicians. The proposed approach uses the gray channels of fundus images and applies the data augmentation technique to create a large dataset of versatile fundus images to train the convolutional neural network model. Using the ResNet-50 architecture, the proposed approach achieved excellent results for detecting glaucoma on the G1020, RIM-ONE, ORIGA, and DRISHTI-GS datasets. We obtained a detection accuracy of 98.48%, a sensitivity of 99.30%, a specificity of 96.52%, an AUC of 97%, and an F1-score of 98% by using the proposed model on the G1020 dataset. The proposed model may help clinicians to diagnose early-stage glaucoma with very high accuracy for timely interventions.
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Affiliation(s)
- Ayesha Shoukat
- Department of Computer Science, Riphah International University, Faisalabad Campus, Faisalabad 44000, Pakistan
| | - Shahzad Akbar
- Department of Computer Science, Riphah International University, Faisalabad Campus, Faisalabad 44000, Pakistan
| | - Syed Ale Hassan
- Department of Computer Science, Riphah International University, Faisalabad Campus, Faisalabad 44000, Pakistan
| | - Sajid Iqbal
- Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al Ahsa 31982, Saudi Arabia
| | - Abid Mehmood
- Department of Management Information Systems, College of Business Administration, King Faisal University, Al Ahsa 31982, Saudi Arabia
| | - Qazi Mudassar Ilyas
- Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al Ahsa 31982, Saudi Arabia
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3
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Development of a Computer System for Automatically Generating a Laser Photocoagulation Plan to Improve the Retinal Coagulation Quality in the Treatment of Diabetic Retinopathy. Symmetry (Basel) 2023. [DOI: 10.3390/sym15020287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
In this article, the development of a computer system for high-tech medical uses in ophthalmology is proposed. An overview of the main methods and algorithms that formed the basis of the coagulation plan planning system is presented. The system provides the formation of a more effective plan for laser coagulation in comparison with the use of existing coagulation techniques. An analysis of monopulse- and pattern-based laser coagulation techniques in the treatment of diabetic retinopathy has shown that modern treatment methods do not provide the required efficacy of medical laser coagulation procedures, as the laser energy is nonuniformly distributed across the pigment epithelium and may exert an excessive effect on parts of the retina and anatomical elements. The analysis has shown that the efficacy of retinal laser coagulation for the treatment of diabetic retinopathy is determined by the relative position of coagulates and parameters of laser exposure. In the course of the development of the computer system proposed herein, main stages of processing diagnostic data were identified. They are as follows: the allocation of the laser exposure zone, the evaluation of laser pulse parameters that would be safe for the fundus, mapping a coagulation plan in the laser exposure zone, followed by the analysis of the generated plan for predicting the therapeutic effect. In the course of the study, it was found that the developed algorithms for placing coagulates in the area of laser exposure provide a more uniform distribution of laser energy across the pigment epithelium when compared to monopulse- and pattern-based laser coagulation techniques.
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4
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Abstract
Topological and geometrical analysis of retinal blood vessels could be a cost-effective way to detect various common diseases. Automated vessel segmentation and vascular tree analysis models require powerful generalization capability in clinical applications. In this work, we constructed a novel benchmark RETA with 81 labelled vessel masks aiming to facilitate retinal vessel analysis. A semi-automated coarse-to-fine workflow was proposed for vessel annotation task. During database construction, we strived to control inter-annotator and intra-annotator variability by means of multi-stage annotation and label disambiguation on self-developed dedicated software. In addition to binary vessel masks, we obtained other types of annotations including artery/vein masks, vascular skeletons, bifurcations, trees and abnormalities. Subjective and objective quality validations of the annotated vessel masks demonstrated significantly improved quality over the existing open datasets. Our annotation software is also made publicly available serving the purpose of pixel-level vessel visualization. Researchers could develop vessel segmentation algorithms and evaluate segmentation performance using RETA. Moreover, it might promote the study of cross-modality tubular structure segmentation and analysis.
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5
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Shi D, Lin Z, Wang W, Tan Z, Shang X, Zhang X, Meng W, Ge Z, He M. A Deep Learning System for Fully Automated Retinal Vessel Measurement in High Throughput Image Analysis. Front Cardiovasc Med 2022; 9:823436. [PMID: 35391847 PMCID: PMC8980780 DOI: 10.3389/fcvm.2022.823436] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 02/22/2022] [Indexed: 11/27/2022] Open
Abstract
Motivation Retinal microvasculature is a unique window for predicting and monitoring major cardiovascular diseases, but high throughput tools based on deep learning for in-detail retinal vessel analysis are lacking. As such, we aim to develop and validate an artificial intelligence system (Retina-based Microvascular Health Assessment System, RMHAS) for fully automated vessel segmentation and quantification of the retinal microvasculature. Results RMHAS achieved good segmentation accuracy across datasets with diverse eye conditions and image resolutions, having AUCs of 0.91, 0.88, 0.95, 0.93, 0.97, 0.95, 0.94 for artery segmentation and 0.92, 0.90, 0.96, 0.95, 0.97, 0.95, 0.96 for vein segmentation on the AV-WIDE, AVRDB, HRF, IOSTAR, LES-AV, RITE, and our internal datasets. Agreement and repeatability analysis supported the robustness of the algorithm. For vessel analysis in quantity, less than 2 s were needed to complete all required analysis.
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Affiliation(s)
- Danli Shi
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Zhihong Lin
- Faculty of Engineering, Monash University, Melbourne, VIC, Australia
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Zachary Tan
- Centre for Eye Research Australia, East Melbourne, VIC, Australia
| | - Xianwen Shang
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Eye Institute, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Xueli Zhang
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Eye Institute, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Wei Meng
- Guangzhou Vision Tech Medical Technology Co., Ltd., Guangzhou, China
| | - Zongyuan Ge
- Research Center and Faculty of Engineering, Monash University, Melbourne, VIC, Australia
| | - Mingguang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- Centre for Eye Research Australia, East Melbourne, VIC, Australia
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Eye Institute, Guangdong Academy of Medical Sciences, Guangzhou, China
- *Correspondence: Mingguang He
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6
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Akbar S, Hassan SA, Shoukat A, Alyami J, Bahaj SA. Detection of microscopic glaucoma through fundus images using deep transfer learning approach. Microsc Res Tech 2022; 85:2259-2276. [PMID: 35170136 DOI: 10.1002/jemt.24083] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 01/05/2022] [Accepted: 01/27/2022] [Indexed: 11/07/2022]
Abstract
Glaucoma disease in humans can lead to blindness if it progresses to the point where it affects the oculus' optic nerve head. It is not easily detected since there are no symptoms, but it can be detected using tonometry, ophthalmoscopy, and perimeter. However, advances in artificial intelligence approaches have permitted machine learning techniques to diagnose at an early stage. Numerous methods have been proposed using Machine Learning to diagnose glaucoma with different data sets and techniques but these are complex methods. Although, medical imaging instruments are used as glaucoma screening methods, fundus imaging specifically is the most used screening technique for glaucoma detection. This study presents a novel DenseNet and DarkNet combination to classify normal and glaucoma affected fundus image. These frameworks have been trained and tested on three data sets of high-resolution fundus (HRF), RIM 1, and ACRIMA. A total of 658 images have been used for healthy eyes and 612 images for glaucoma-affected eyes classification. It has also been observed that the fusion of DenseNet and DarkNet outperforms the two CNN networks and achieved 99.7% accuracy, 98.9% sensitivity, 100% specificity for the HRF database. In contrast, for the RIM1 database, 89.3% accuracy, 93.3% sensitivity, 88.46% specificity has been attained. Moreover, for the ACRIMA database, 99% accuracy, 100% sensitivity, 99% specificity has been achieved. Therefore, the proposed method is robust and efficient with less computational time and complexity compared to the literature available.
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Affiliation(s)
- Shahzad Akbar
- Riphah College of Computing, Riphah International University, Faisalabad Campus, Faisalabad, Pakistan
| | - Syed Ale Hassan
- Riphah College of Computing, Riphah International University, Faisalabad Campus, Faisalabad, Pakistan
| | - Ayesha Shoukat
- Riphah College of Computing, Riphah International University, Faisalabad Campus, Faisalabad, Pakistan
| | - Jaber Alyami
- Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.,Imaging Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
| | - Saeed Ali Bahaj
- MIS Department, College of Business Administration, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia
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7
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Gour N, Tanveer M, Khanna P. Challenges for ocular disease identification in the era of artificial intelligence. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06770-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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8
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Sen S, Kannan NB, Kumar J, Rajan RP, Kumar K, Baliga G, Reddy H, Upadhyay A, Ramasamy K. Retinal manifestations in patients with SARS-CoV-2 infection and pathogenetic implications: a systematic review. Int Ophthalmol 2021; 42:323-336. [PMID: 34379290 PMCID: PMC8356207 DOI: 10.1007/s10792-021-01996-7] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 07/30/2021] [Indexed: 02/08/2023]
Abstract
Introduction The pandemic of COVID-19 has been caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Apart from respiratory malfunction, COVID-19 causes a system-wide thromboembolic state, leading to serious cardiovascular, cerebrovascular and peripheral vascular manifestations. However, our knowledge regarding retinal manifestations due to systemic COVID-19 is minimal. This systematic review has comprehensively summarized all retinal manifestations secondary to COVID-19 disease recorded till date since the beginning of the pandemic. Methods All studies published till November 27, 2020, which have reported retinal manifestations in COVID-19 patients were systematically reviewed using the PRISMA statement. Results We included 15 articles: 11 case reports and four cross-sectional case series. The most commonly reported manifestations which did not affect visual acuity were retinal hemorrhages and cotton wool spots. The most common vision threatening manifestation was retinal vein occlusion with associated macular edema. Rarely, patients may also present with retinal arterial occlusions and ocular inflammation. These manifestations may occur from as soon as within a week after the onset of COVID-19 symptoms to more than 6 weeks after. Conclusion Mostly causing milder disease, COVID-19 may however lead to severe life-threatening thromboembolic complications, and systemic antithrombotic therapy has been suggested as a prophylactic and therapeutic management strategy for patients affected with serious systemic disease. However, both sick and apparently healthy patients may suffer from various retinal complications which may lead to loss of vision as well. No consensus regarding management of retinal complications with anticoagulants or anti-inflammatory medications have been proposed; however, they may be tackled on individual basis.
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Affiliation(s)
- Sagnik Sen
- Department of Retina and Vitreous, Aravind Eye Hospital, Madurai, India.
| | | | - Jayant Kumar
- Department of Retina and Vitreous, Aravind Eye Hospital, Madurai, India
| | - Renu P Rajan
- Department of Retina and Vitreous, Aravind Eye Hospital, Madurai, India
| | - Karthik Kumar
- Department of Retina and Vitreous, Aravind Eye Hospital, Madurai, India
| | - Girish Baliga
- Department of Retina and Vitreous, Aravind Eye Hospital, Madurai, India
| | | | - Anubhav Upadhyay
- Department of Retina and Vitreous, Aravind Eye Hospital, Madurai, India
| | - Kim Ramasamy
- Department of Retina and Vitreous, Aravind Eye Hospital, Madurai, India
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9
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Saba T, Akbar S, Kolivand H, Ali Bahaj S. Automatic detection of papilledema through fundus retinal images using deep learning. Microsc Res Tech 2021; 84:3066-3077. [PMID: 34236733 DOI: 10.1002/jemt.23865] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Revised: 04/22/2021] [Accepted: 05/29/2021] [Indexed: 11/09/2022]
Abstract
Papilledema is a syndrome of the retina in which retinal optic nerve is inflated by elevation of intracranial pressure. The papilledema abnormalities such as retinal nerve fiber layer (RNFL) opacification may lead to blindness. These abnormalities could be seen through capturing of retinal images by means of fundus camera. This paper presents a deep learning-based automated system that detects and grades the papilledema through U-Net and Dense-Net architectures. The proposed approach has two main stages. First, optic disc and its surrounding area in fundus retinal image are localized and cropped for input to Dense-Net which classifies the optic disc as papilledema or normal. Second, consists of preprocessing of Dense-Net classified papilledema fundus image by Gabor filter. The preprocessed papilledema image is input to U-Net to achieve the segmented vascular network from which the vessel discontinuity index (VDI) and vessel discontinuity index to disc proximity (VDIP) are calculated for grading of papilledema. The VDI and VDIP are standard parameter to check the severity and grading of papilledema. The proposed system is evaluated on 60 papilledema and 40 normal fundus images taken from STARE dataset. The experimental results for classification of papilledema through Dense-Net are much better in terms of sensitivity 98.63%, specificity 97.83%, and accuracy 99.17%. Similarly, the grading results for mild and severe papilledema classification through U-Net are also much better in terms of sensitivity 99.82%, specificity 98.65%, and accuracy 99.89%. The deep learning-based automated detection and grading of papilledema for clinical purposes is first effort in state of art.
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Affiliation(s)
- Tanzila Saba
- Artificial Intelligence & Data Analytics (AIDA) Lab CCIS, Prince Sultan University, Riyadh, 11586, Saudi Arabia
| | - Shahzad Akbar
- Department of Computing, Riphah International University, Faisalabad Campus, Faisalabad, 38000, Pakistan
| | - Hoshang Kolivand
- School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, United Kingdom.,School of Computing and Digital Technologies, Staffordshire University, Staffordshire, United Kingdom
| | - Saeed Ali Bahaj
- MIS Department, College of Business Administration, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
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10
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Dataset from fundus images for the study of diabetic retinopathy. Data Brief 2021; 36:107068. [PMID: 34307801 PMCID: PMC8257963 DOI: 10.1016/j.dib.2021.107068] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/02/2021] [Accepted: 04/13/2021] [Indexed: 11/24/2022] Open
Abstract
This article presents a database containing 757 color fundus images acquired at the Department of Ophthalmology of the Hospital de Clínicas, Facultad de Ciencias Médicas (FCM), Universidad Nacional de Asunción (UNA), Paraguay. Firstly, the retinal images were acquired with a clinical procedure presented in this paper. The acquisition of the retinographies was made through the Visucam 500 camera of the Zeiss brand. Next, two expert ophthalmologists have classified the dataset. These data can help physicians and researchers in the detection of cases of Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR), in their different stages. The dataset generated will be useful for ophthalmologists and researchers to work on automatic detection algorithms for Diabetic Retinopathy (DR).
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11
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Liao Y, Xia H, Song S, Li H. Microaneurysm detection in fundus images based on a novel end-to-end convolutional neural network. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.04.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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12
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NAIR ARUNT, MUTHUVEL K. AUTOMATED SCREENING OF DIABETIC RETINOPATHY WITH OPTIMIZED DEEP CONVOLUTIONAL NEURAL NETWORK: ENHANCED MOTH FLAME MODEL. J MECH MED BIOL 2021. [DOI: 10.1142/s0219519421500056] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Nowadays, analysis on retinal image exists as one of the challenging area for study. Numerous retinal diseases could be recognized by analyzing the variations taking place in retina. However, the main disadvantage among those studies is that, they do not have higher recognition accuracy. The proposed framework includes four phases namely, (i) Blood Vessel Segmentation (ii) Feature Extraction (iii) Optimal Feature Selection and (iv) Classification. Initially, the input fundus image is subjected to blood vessel segmentation from which two binary thresholded images (one from High Pass Filter (HPF) and other from top-hat reconstruction) are acquired. These two images are differentiated and the areas that are common to both are said to be the major vessels and the left over regions are fused to form vessel sub-image. These vessel sub-images are classified with Gaussian Mixture Model (GMM) classifier and the resultant is summed up with the major vessels to form the segmented blood vessels. The segmented images are subjected to feature extraction process, where the features like proposed Local Binary Pattern (LBP), Gray-Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRM) are extracted. As the curse of dimensionality seems to be the greatest issue, it is important to select the appropriate features from the extracted one for classification. In this paper, a new improved optimization algorithm Moth Flame with New Distance Formulation (MF-NDF) is introduced for selecting the optimal features. Finally, the selected optimal features are subjected to Deep Convolutional Neural Network (DCNN) model for classification. Further, in order to make the precise diagnosis, the weights of DCNN are optimally tuned by the same optimization algorithm. The performance of the proposed algorithm will be compared against the conventional algorithms in terms of positive and negative measures.
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Affiliation(s)
- ARUN T NAIR
- Department of Electrical and Electronics Engineering, Noorul Islam Centre for Higher Education, Kumaracoil 629180, Tamil Nadu, India
| | - K. MUTHUVEL
- Department of Electrical and Electronics Engineering, Noorul Islam Centre for Higher Education, Kumaracoil 629180, Tamil Nadu, India
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13
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Landecho MF, Yuste JR, Gándara E, Sunsundegui P, Quiroga J, Alcaide AB, García-Layana A. COVID-19 retinal microangiopathy as an in vivo biomarker of systemic vascular disease? J Intern Med 2021; 289:116-120. [PMID: 32729633 DOI: 10.1111/joim.13156] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 06/30/2020] [Accepted: 06/30/2020] [Indexed: 12/21/2022]
Abstract
IMPORTANCE COVID-19 is caused by SARS-CoV-2, a betacoronavirus that uses the angiotensin-converting enzyme-related carboxypeptidase (ACE2) receptor to gain entry into cells. ACE2 receptor is widely expressed in multiple organs, including the retina, an extension of the central nervous system. The ACE2 receptor is involved in the diabetic and hypertensive retinopathy. Additionally, coronaviruses cause ocular infections in animals, including retinitis, and optic neuritis. OBJECTIVE To assess whether there is any retinal disease associated with COVID-19. DESIGN We have evaluated 27 asymptomatic subjects, with retinal fundoscopic, optical coherence tomography (OCT) and OCT angiography fourteen days after hospital discharge due to COVID-19 bilateral pneumonia. RESULTS Cotton wool exudates were evident in six out of 27 patients evaluated, a 22%. Cotton wool exudates are a marker vascular disease severity in other medical context, that is diabetes and hypertension, and are associated with increased risk for acute vascular events. Whether antiaggregation therapy may play a role on fundoscopic-selected patients with COVID-19 requires prospective trials.
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Affiliation(s)
- M F Landecho
- From the, Covid19 Department, Clinica Universidad de Navarra, Pamplona, Navarra, Spain.,Internal Medicine Department, Clinica Universidad de Navarra, Pamplona, Navarra, Spain
| | - J R Yuste
- From the, Covid19 Department, Clinica Universidad de Navarra, Pamplona, Navarra, Spain.,Internal Medicine Department, Clinica Universidad de Navarra, Pamplona, Navarra, Spain.,Microbiology and Infectious Diseases Division, Clinica Universidad de Navarra, Pamplona, Navarra, Spain
| | - E Gándara
- Ophtalmology Department, Clinica Universidad de Navarra, Pamplona, Navarra, Spain
| | - P Sunsundegui
- From the, Covid19 Department, Clinica Universidad de Navarra, Pamplona, Navarra, Spain.,Internal Medicine Department, Clinica Universidad de Navarra, Pamplona, Navarra, Spain
| | - J Quiroga
- From the, Covid19 Department, Clinica Universidad de Navarra, Pamplona, Navarra, Spain.,Internal Medicine Department, Clinica Universidad de Navarra, Pamplona, Navarra, Spain.,CIBEREHD
| | - A B Alcaide
- From the, Covid19 Department, Clinica Universidad de Navarra, Pamplona, Navarra, Spain.,Pulmonary Medicine Department, Clinica Universidad de Navarra, Pamplona, Navarra, Spain
| | - A García-Layana
- Ophtalmology Department, Clinica Universidad de Navarra, Pamplona, Navarra, Spain
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14
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Marinho PM, Marcos AA, Branco AMPC, Sakamoto V, Romano A, Schor P, Farah ME, Nascimento H, Belfort R. Results from the SERPICO-19 study - the role of retinal evaluation and in vivo vascular assessment in COVID-19. EClinicalMedicine 2020; 29:100655. [PMID: 33251502 PMCID: PMC7678456 DOI: 10.1016/j.eclinm.2020.100655] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 11/02/2020] [Accepted: 11/06/2020] [Indexed: 11/30/2022] Open
Affiliation(s)
- Paula M. Marinho
- Instituto Paulista de Estudos e Pesquisas em Oftalmologia – IPEPO/Instituto da Visão, São Paulo, Brazil
- Ophthalmology Department, Federal University of São Paulo, Hospital São Paulo, São Paulo, Brazil
| | - Allexya A.A. Marcos
- Instituto Paulista de Estudos e Pesquisas em Oftalmologia – IPEPO/Instituto da Visão, São Paulo, Brazil
- Ophthalmology Department, Federal University of São Paulo, Hospital São Paulo, São Paulo, Brazil
| | | | - Victoria Sakamoto
- Ophthalmology Department, Federal University of São Paulo, Hospital São Paulo, São Paulo, Brazil
| | - André Romano
- Instituto Paulista de Estudos e Pesquisas em Oftalmologia – IPEPO/Instituto da Visão, São Paulo, Brazil
- Ophthalmology Department, Federal University of São Paulo, Hospital São Paulo, São Paulo, Brazil
| | - Paulo Schor
- Instituto Paulista de Estudos e Pesquisas em Oftalmologia – IPEPO/Instituto da Visão, São Paulo, Brazil
- Ophthalmology Department, Federal University of São Paulo, Hospital São Paulo, São Paulo, Brazil
| | - Michel E. Farah
- Instituto Paulista de Estudos e Pesquisas em Oftalmologia – IPEPO/Instituto da Visão, São Paulo, Brazil
- Ophthalmology Department, Federal University of São Paulo, Hospital São Paulo, São Paulo, Brazil
| | - Heloisa Nascimento
- Instituto Paulista de Estudos e Pesquisas em Oftalmologia – IPEPO/Instituto da Visão, São Paulo, Brazil
- Ophthalmology Department, Federal University of São Paulo, Hospital São Paulo, São Paulo, Brazil
- Corresponding author. Present address: Vision Institute and Federal, University of São Paulo, Rua Botucatu, 822. São Paulo, Brazil.
| | - Rubens Belfort
- Instituto Paulista de Estudos e Pesquisas em Oftalmologia – IPEPO/Instituto da Visão, São Paulo, Brazil
- Ophthalmology Department, Federal University of São Paulo, Hospital São Paulo, São Paulo, Brazil
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15
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Huang Z, Tang C, Xu M, Lei Z. Joint Retinex-based variational model and CLAHE-in-CIELUV for enhancement of low-quality color retinal images. APPLIED OPTICS 2020; 59:8628-8637. [PMID: 33104544 DOI: 10.1364/ao.401792] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 08/27/2020] [Indexed: 06/11/2023]
Abstract
Poor visual quality of color retinal images greatly interferes with the analysis and diagnosis of the ophthalmologist. In this paper, we propose an enhancement method for low-quality color retinal images based on the combination of the Retinex-based enhancement method and the contrast limited adaptive histogram equalization (CLAHE) algorithm. More specifically, we first estimate the illumination map of the entire image by constructing a Retinex-based variational model. Then, we restore the reflectance map by removing the illumination modified by Gamma correction and directly enable the reflectance as the initial enhancement. To further enhance the clarity and contrast of blood vessels while avoiding color distortion, we apply CLAHE on the luminance channel in CIELUV color space. We collect 60 low-quality color retinal images as our test dataset to verify the reliability of our proposed method. Experimental results show that the proposed method is superior to the other three related methods, both in terms of visual analysis and quantitative evaluation while testing on our dataset. Additionally, we apply the proposed method to four publicly available datasets, and the results show that our methods may be helpful for the detection and analysis of retinopathy.
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