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Amin J, Shazadi I, Sharif M, Yasmin M, Almujally NA, Nam Y. Localization and grading of NPDR lesions using ResNet-18-YOLOv8 model and informative features selection for DR classification based on transfer learning. Heliyon 2024; 10:e30954. [PMID: 38779022 PMCID: PMC11109848 DOI: 10.1016/j.heliyon.2024.e30954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 05/04/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
Complications in diabetes lead to diabetic retinopathy (DR) hence affecting the vision. Computerized methods performed a significant role in DR detection at the initial phase to cure vision loss. Therefore, a method is proposed in this study that consists of three models for localization, segmentation, and classification. A novel technique is designed with the combination of pre-trained ResNet-18 and YOLOv8 models based on the selection of optimum layers for the localization of DR lesions. The localized images are passed to the designed semantic segmentation model on selected layers and trained on optimized learning hyperparameters. The segmentation model performance is evaluated on the Grand-challenge IDRID segmentation dataset. The achieved results are computed in terms of mean IoU 0.95,0.94, 0.96, 0.94, and 0.95 on OD, SoftExs, HardExs, HAE, and MAs respectively. Another classification model is developed in which deep features are derived from the pre-trained Efficientnet-b0 model and optimized using a Genetic algorithm (GA) based on the selected parameters for grading of NPDR lesions. The proposed model achieved greater than 98 % accuracy which is superior to previous methods.
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Affiliation(s)
- Javaria Amin
- Department of Computer Science, University of Wah, Wah Cantt, Pakistan
| | - Irum Shazadi
- Department of Computer Science, University of Wah, Wah Cantt, Pakistan
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
| | - Mussarat Yasmin
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
| | - Nouf Abdullah Almujally
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Yunyoung Nam
- Department of ICT Convergence, Soonchunhyang University, Asan, 31538, South Korea
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2
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Verma PK, Kaur J. Systematic Review of Retinal Blood Vessels Segmentation Based on AI-driven Technique. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01010-3. [PMID: 38438695 DOI: 10.1007/s10278-024-01010-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/07/2023] [Accepted: 12/13/2023] [Indexed: 03/06/2024]
Abstract
Image segmentation is a crucial task in computer vision and image processing, with numerous segmentation algorithms being found in the literature. It has important applications in scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, image compression, among others. In light of this, the widespread popularity of deep learning (DL) and machine learning has inspired the creation of fresh methods for segmenting images using DL and ML models respectively. We offer a thorough analysis of this recent literature, encompassing the range of ground-breaking initiatives in semantic and instance segmentation, including convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid-based methods, recurrent networks, visual attention models, and generative models in adversarial settings. We study the connections, benefits, and importance of various DL- and ML-based segmentation models; look at the most popular datasets; and evaluate results in this Literature.
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Affiliation(s)
- Prem Kumari Verma
- Department of Computer Science and Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, 144008, Punjab, India.
| | - Jagdeep Kaur
- Department of Computer Science and Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, 144008, Punjab, India
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3
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Karsaz A. A modified convolutional neural network architecture for diabetic retinopathy screening using SVDD. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109102] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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4
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Diabetic retinopathy screening using improved support vector domain description: a clinical study. Soft comput 2022. [DOI: 10.1007/s00500-022-07387-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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5
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Kashyap D, Pal D, Sharma R, Garg VK, Goel N, Koundal D, Zaguia A, Koundal S, Belay A. Global Increase in Breast Cancer Incidence: Risk Factors and Preventive Measures. BIOMED RESEARCH INTERNATIONAL 2022; 2022:9605439. [PMID: 35480139 PMCID: PMC9038417 DOI: 10.1155/2022/9605439] [Citation(s) in RCA: 139] [Impact Index Per Article: 69.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 02/25/2022] [Accepted: 03/21/2022] [Indexed: 02/07/2023]
Abstract
Breast cancer is a global cause for concern owing to its high incidence around the world. The alarming increase in breast cancer cases emphasizes the management of disease at multiple levels. The management should start from the beginning that includes stringent cancer screening or cancer registry to effective diagnostic and treatment strategies. Breast cancer is highly heterogeneous at morphology as well as molecular levels and needs different therapeutic regimens based on the molecular subtype. Breast cancer patients with respective subtype have different clinical outcome prognoses. Breast cancer heterogeneity emphasizes the advanced molecular testing that will help on-time diagnosis and improved survival. Emerging fields such as liquid biopsy and artificial intelligence would help to under the complexity of breast cancer disease and decide the therapeutic regimen that helps in breast cancer management. In this review, we have discussed various risk factors and advanced technology available for breast cancer diagnosis to combat the worst breast cancer status and areas that need to be focused for the better management of breast cancer.
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Affiliation(s)
- Dharambir Kashyap
- Department of Histopathology, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India
| | - Deeksha Pal
- Department of Translational and Regenerative Medicine, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India
| | - Riya Sharma
- Department of Pulmonary Medicine, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India
| | - Vivek Kumar Garg
- Department of Medical Laboratory Technology, University Institute of Applied Health Sciences, Chandigarh University (Gharuan), Mohali 140313, India
| | - Neelam Goel
- Department of Information Technology, University Institute of Engineering & Technology, Panjab University, Chandigarh 160014, India
| | - Deepika Koundal
- Department of Systemics, School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India
| | - Atef Zaguia
- Department of computer science, College of Computers and Information Technology, Taif University, P.O. BOX 11099, Taif 21944, Saudi Arabia
| | - Shubham Koundal
- Department of Medical Laboratory Technology, University Institute of Applied Health Sciences, Chandigarh University (Gharuan), Mohali 140313, India
| | - Assaye Belay
- Department of Statistics, Mizan-Tepi University, Ethiopia
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6
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Al-Mukhtar M, Morad AH, Albadri M, Islam MDS. Weakly Supervised Sensitive Heatmap framework to classify and localize diabetic retinopathy lesions. Sci Rep 2021; 11:23631. [PMID: 34880311 PMCID: PMC8655092 DOI: 10.1038/s41598-021-02834-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Accepted: 11/11/2021] [Indexed: 11/09/2022] Open
Abstract
Vision loss happens due to diabetic retinopathy (DR) in severe stages. Thus, an automatic detection method applied to diagnose DR in an earlier phase may help medical doctors to make better decisions. DR is considered one of the main risks, leading to blindness. Computer-Aided Diagnosis systems play an essential role in detecting features in fundus images. Fundus images may include blood vessels, exudates, micro-aneurysm, hemorrhages, and neovascularization. In this paper, our model combines automatic detection for the diabetic retinopathy classification with localization methods depending on weakly-supervised learning. The model has four stages; in stage one, various preprocessing techniques are applied to smooth the data set. In stage two, the network had gotten deeply to the optic disk segment for eliminating any exudate's false prediction because the exudates had the same color pixel as the optic disk. In stage three, the network is fed through training data to classify each label. Finally, the layers of the convolution neural network are re-edited, and used to localize the impact of DR on the patient's eye. The framework tackles the matching technique between two essential concepts where the classification problem depends on the supervised learning method. While the localization problem was obtained by the weakly supervised method. An additional layer known as weakly supervised sensitive heat map (WSSH) was added to detect the ROI of the lesion at a test accuracy of 98.65%, while comparing with Class Activation Map that involved weakly supervised technology achieved 0.954. The main purpose is to learn a representation that collect the central localization of discriminative features in a retina image. CNN-WSSH model is able to highlight decisive features in a single forward pass for getting the best detection of lesions.
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Affiliation(s)
| | | | | | - M D Samiul Islam
- Department of Computing Science, University of Alberta, Edmonton, Canada.
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7
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Janakiraman S, M. DP, A. CJM, S. K, P. AR. Reliable IoT-based Health-care System for Diabetic Retinopathy Diagnosis to defend the Vision of Patients. INTERNATIONAL JOURNAL OF PERVASIVE COMPUTING AND COMMUNICATIONS 2021. [DOI: 10.1108/ijpcc-08-2020-0109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
The purpose of this paper is to design an Internet-of-Things (IoT) architecture-based Diabetic Retinopathy Detection Scheme (DRDS) proposed for identifying Type-I or Type-II diabetes and to specifically advise the Type-II diabetic patients about the possibility of vision loss.
Design/methodology/approach
The proposed DRDS includes the benefits of automatic calculation of clip limit parameters and sub-window for making the detection process completely adaptive. It uses the advantages of extended 5 × 5 Sobels operator for estimating the maximum edges determined through the convolution of 24 pixels with eight templates to achieve 24 outputs corresponding to individual pixels for finding the maximum magnitude. It enhances the probability of connecting pixels in the vascular map with its closely located neighbourhood points in the fundus images. Then, the spatial information and kernel of the neighbourhood pixels are integrated through the Robust Semi-supervised Kernelized Fuzzy Local information C-Means Clustering (RSKFL-CMC) method to attain significant clustering process.
Findings
The results of the proposed DRDS architecture confirm the predominance in terms of accuracy, specificity and sensitivity. The proposed DRDS technique facilitates superior performance at an average of 99.64% accuracy, 76.84% sensitivity and 99.93% specificity.
Research limitations/implications
DRDS is proposed as a comfortable, pain-free and harmless diagnosis system using the merits of Dexcom G4 Plantinum sensors for estimating blood glucose level in diabetic patients. It uses the merits of RSKFL-CMC method to estimate the spatial information and kernel of the neighborhood pixels for attaining significant clustering process.
Practical implications
The IoT architecture comprises of the application layer that inherits the DR application enabled Graphical User Interface (GUI) which is combined for processing of fundus images by using MATLAB applications. This layer aids the patients in storing the capture fundus images in the database for future diagnosis.
Social implications
This proposed DRDS method plays a vital role in the detection of DR and categorization based on the intensity of disease into severe, moderate and mild grades. The proposed DRDS is responsible for preventing vision loss of diabetic Type-II patients by accurate and potential detection achieved through the utilization of IoT architecture.
Originality/value
The performance of the proposed scheme with the benchmarked approaches of the literature is implemented using MATLAB R2010a. The complete evaluations of the proposed scheme are conducted using HRF, REVIEW, STARE and DRIVE data sets with subjective quantification provided by the experts for the purpose of potential retinal blood vessel segmentation.
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8
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Gilbert MJ, Sun JK. Artificial Intelligence in the assessment of diabetic retinopathy from fundus photographs. Semin Ophthalmol 2021; 35:325-332. [PMID: 33539253 DOI: 10.1080/08820538.2020.1855358] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Background: Over the next 25 years, the global prevalence of diabetes is expected to grow to affect 700 million individuals. Consequently, an unprecedented number of patients will be at risk for vision loss from diabetic eye disease. This demand will almost certainly exceed the supply of eye care professionals to individually evaluate each patient on an annual basis, signaling the need for 21st century tools to assist our profession in meeting this challenge. Methods: Review of available literature on artificial intelligence (AI) as applied to diabetic retinopathy (DR) detection and predictionResults: The field of AI has seen exponential growth in evaluating fundus photographs for DR. AI systems employ machine learning and artificial neural networks to teach themselves how to grade DR from libraries of tens of thousands of images and may be able to predict future DR progression based on baseline fundus photographs. Conclusions: AI algorithms are highly promising for the purposes of DR detection and will likely be able to reliably predict DR worsening in the future. A deeper understanding of these systems and how they interpret images is critical as they transition from the bench into the clinic.
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Affiliation(s)
- Michael J Gilbert
- Joslin Diabetes Center, Beetham Eye Institute , Boston, MA, United States
| | - Jennifer K Sun
- Joslin Diabetes Center, Beetham Eye Institute , Boston, MA, United States.,Department of Ophthalmology, Harvard Medical School , Boston, MA, United States
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9
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Chalakkal R, Hafiz F, Abdulla W, Swain A. An efficient framework for automated screening of Clinically Significant Macular Edema. Comput Biol Med 2020; 130:104128. [PMID: 33529843 DOI: 10.1016/j.compbiomed.2020.104128] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 11/09/2020] [Accepted: 11/16/2020] [Indexed: 11/20/2022]
Abstract
The present study proposes a new approach to automated screening of Clinically Significant Macular Edema (CSME) and addresses two major challenges associated with such screenings, i.e., exudate segmentation and imbalanced datasets. The proposed approach replaces the conventional exudate segmentation based feature extraction by combining a pre-trained deep neural network with meta-heuristic feature selection. A feature space over-sampling technique is being used to overcome the effects of skewed datasets and the screening is accomplished by a k-NN based classifier. The role of each data-processing step (e.g., class balancing, feature selection) and the effects of limiting the region of interest to fovea on the classification performance are critically analyzed. Finally, the selection and implication of operating points on Receiver Operating Characteristic curve are discussed. The results of this study convincingly demonstrate that by following these fundamental practices of machine learning, a basic k-NN based classifier could effectively accomplish the CSME screening.
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Affiliation(s)
- Renoh Chalakkal
- Department of Electrical & Computer Engineering, The University of Auckland, Auckland, New Zealand; oDocs Eye Care, Dunedin, New Zealand.
| | - Faizal Hafiz
- Department of Electrical & Computer Engineering, The University of Auckland, Auckland, New Zealand; oDocs Eye Care, Dunedin, New Zealand.
| | - Waleed Abdulla
- Department of Electrical & Computer Engineering, The University of Auckland, Auckland, New Zealand.
| | - Akshya Swain
- Department of Electrical & Computer Engineering, The University of Auckland, Auckland, New Zealand.
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10
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Romero-Oraá R, García M, Oraá-Pérez J, López-Gálvez MI, Hornero R. Effective Fundus Image Decomposition for the Detection of Red Lesions and Hard Exudates to Aid in the Diagnosis of Diabetic Retinopathy. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6549. [PMID: 33207825 PMCID: PMC7698181 DOI: 10.3390/s20226549] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/07/2020] [Accepted: 11/13/2020] [Indexed: 06/11/2023]
Abstract
Diabetic retinopathy (DR) is characterized by the presence of red lesions (RLs), such as microaneurysms and hemorrhages, and bright lesions, such as exudates (EXs). Early DR diagnosis is paramount to prevent serious sight damage. Computer-assisted diagnostic systems are based on the detection of those lesions through the analysis of fundus images. In this paper, a novel method is proposed for the automatic detection of RLs and EXs. As the main contribution, the fundus image was decomposed into various layers, including the lesion candidates, the reflective features of the retina, and the choroidal vasculature visible in tigroid retinas. We used a proprietary database containing 564 images, randomly divided into a training set and a test set, and the public database DiaretDB1 to verify the robustness of the algorithm. Lesion detection results were computed per pixel and per image. Using the proprietary database, 88.34% per-image accuracy (ACCi), 91.07% per-pixel positive predictive value (PPVp), and 85.25% per-pixel sensitivity (SEp) were reached for the detection of RLs. Using the public database, 90.16% ACCi, 96.26% PPV_p, and 84.79% SEp were obtained. As for the detection of EXs, 95.41% ACCi, 96.01% PPV_p, and 89.42% SE_p were reached with the proprietary database. Using the public database, 91.80% ACCi, 98.59% PPVp, and 91.65% SEp were obtained. The proposed method could be useful to aid in the diagnosis of DR, reducing the workload of specialists and improving the attention to diabetic patients.
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Affiliation(s)
- Roberto Romero-Oraá
- Biomedical Engineering Group, Universidad de Valladolid, 47011 Valladolid, Spain; (M.G.); (J.O.-P.); (M.I.L.-G.); (R.H.)
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
| | - María García
- Biomedical Engineering Group, Universidad de Valladolid, 47011 Valladolid, Spain; (M.G.); (J.O.-P.); (M.I.L.-G.); (R.H.)
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
| | - Javier Oraá-Pérez
- Biomedical Engineering Group, Universidad de Valladolid, 47011 Valladolid, Spain; (M.G.); (J.O.-P.); (M.I.L.-G.); (R.H.)
| | - María I. López-Gálvez
- Biomedical Engineering Group, Universidad de Valladolid, 47011 Valladolid, Spain; (M.G.); (J.O.-P.); (M.I.L.-G.); (R.H.)
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
- Department of Ophthalmology, Hospital Clínico Universitario de Valladolid, 47003 Valladolid, Spain
- Instituto Universitario de Oftalmobiología Aplicada (IOBA), Universidad de Valladolid, 47011 Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group, Universidad de Valladolid, 47011 Valladolid, Spain; (M.G.); (J.O.-P.); (M.I.L.-G.); (R.H.)
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
- Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, 47011 Valladolid, Spain
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11
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Thomas SA, Titus G. Design of a portable retinal imaging module with automatic abnormality detection. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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12
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Stolte S, Fang R. A survey on medical image analysis in diabetic retinopathy. Med Image Anal 2020; 64:101742. [PMID: 32540699 DOI: 10.1016/j.media.2020.101742] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 02/03/2020] [Accepted: 05/28/2020] [Indexed: 01/12/2023]
Abstract
Diabetic Retinopathy (DR) represents a highly-prevalent complication of diabetes in which individuals suffer from damage to the blood vessels in the retina. The disease manifests itself through lesion presence, starting with microaneurysms, at the nonproliferative stage before being characterized by neovascularization in the proliferative stage. Retinal specialists strive to detect DR early so that the disease can be treated before substantial, irreversible vision loss occurs. The level of DR severity indicates the extent of treatment necessary - vision loss may be preventable by effective diabetes management in mild (early) stages, rather than subjecting the patient to invasive laser surgery. Using artificial intelligence (AI), highly accurate and efficient systems can be developed to help assist medical professionals in screening and diagnosing DR earlier and without the full resources that are available in specialty clinics. In particular, deep learning facilitates diagnosis earlier and with higher sensitivity and specificity. Such systems make decisions based on minimally handcrafted features and pave the way for personalized therapies. Thus, this survey provides a comprehensive description of the current technology used in each step of DR diagnosis. First, it begins with an introduction to the disease and the current technologies and resources available in this space. It proceeds to discuss the frameworks that different teams have used to detect and classify DR. Ultimately, we conclude that deep learning systems offer revolutionary potential to DR identification and prevention of vision loss.
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Affiliation(s)
- Skylar Stolte
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, 1275 Center Drive, Biomedical Sciences Building JG56 P.O. Box 116131 Gainesville, FL 32611-6131, USA.
| | - Ruogu Fang
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, 1275 Center Drive, Biomedical Sciences Building JG56 P.O. Box 116131 Gainesville, FL 32611-6131, USA.
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13
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Tong Y, Lu W, Yu Y, Shen Y. Application of machine learning in ophthalmic imaging modalities. EYE AND VISION 2020; 7:22. [PMID: 32322599 PMCID: PMC7160952 DOI: 10.1186/s40662-020-00183-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 03/10/2020] [Indexed: 12/27/2022]
Abstract
In clinical ophthalmology, a variety of image-related diagnostic techniques have begun to offer unprecedented insights into eye diseases based on morphological datasets with millions of data points. Artificial intelligence (AI), inspired by the human multilayered neuronal system, has shown astonishing success within some visual and auditory recognition tasks. In these tasks, AI can analyze digital data in a comprehensive, rapid and non-invasive manner. Bioinformatics has become a focus particularly in the field of medical imaging, where it is driven by enhanced computing power and cloud storage, as well as utilization of novel algorithms and generation of data in massive quantities. Machine learning (ML) is an important branch in the field of AI. The overall potential of ML to automatically pinpoint, identify and grade pathological features in ocular diseases will empower ophthalmologists to provide high-quality diagnosis and facilitate personalized health care in the near future. This review offers perspectives on the origin, development, and applications of ML technology, particularly regarding its applications in ophthalmic imaging modalities.
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Affiliation(s)
- Yan Tong
- 1Eye Center, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China
| | - Wei Lu
- 1Eye Center, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China
| | - Yue Yu
- 1Eye Center, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China
| | - Yin Shen
- 1Eye Center, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China.,2Medical Research Institute, Wuhan University, Wuhan, Hubei China
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14
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A metaheuristic segmentation framework for detection of retinal disorders from fundus images using a hybrid ant colony optimization. Soft comput 2020. [DOI: 10.1007/s00500-020-04753-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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15
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Javidi M, Harati A, Pourreza H. Retinal image assessment using bi-level adaptive morphological component analysis. Artif Intell Med 2019; 99:101702. [PMID: 31606110 DOI: 10.1016/j.artmed.2019.07.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Revised: 07/25/2019] [Accepted: 07/26/2019] [Indexed: 10/26/2022]
Abstract
The automated analysis of retinal images is a widely researched area which can help to diagnose several diseases like diabetic retinopathy in early stages of the disease. More specifically, separation of vessels and lesions is very critical as features of these structures are directly related to the diagnosis and treatment process of diabetic retinopathy. The complexity of the retinal image contents especially in images with severe diabetic retinopathy makes detection of vascular structure and lesions difficult. In this paper, a novel framework based on morphological component analysis (MCA) is presented which benefits from the adaptive representations obtained via dictionary learning. In the proposed Bi-level Adaptive MCA (BAMCA), MCA is extended to locally deal with sparse representation of the retinal images at patch level whereas the decomposition process occurs globally at the image level. BAMCA method with appropriately offline learnt dictionaries is adopted to work on retinal images with severe diabetic retinopathy in order to simultaneously separate vessels and exudate lesions as diagnostically useful morphological components. To obtain the appropriate dictionaries, K-SVD dictionary learning algorithm is modified to use a gated error which guides the process toward learning the main structures of the retinal images using vessel or lesion maps. Computational efficiency of the proposed framework is also increased significantly through some improvement leading to noticeable reduction in run time. We experimentally show how effective dictionaries can be learnt which help BAMCA to successfully separate exudate and vessel components from retinal images even in severe cases of diabetic retinopathy. In this paper, in addition to visual qualitative assessment, the performance of the proposed method is quantitatively measured in the framework of vessel and exudate segmentation. The reported experimental results on public datasets demonstrate that the obtained components can be used to achieve competitive results with regard to the state-of-the-art vessel and exudate segmentation methods.
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Affiliation(s)
- Malihe Javidi
- Computer Engineering Department, Quchan University of Technology, Quchan, Iran.
| | - Ahad Harati
- Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
| | - HamidReza Pourreza
- Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
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16
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Porwal P, Pachade S, Kokare M, Deshmukh G, Son J, Bae W, Liu L, Wang J, Liu X, Gao L, Wu T, Xiao J, Wang F, Yin B, Wang Y, Danala G, He L, Choi YH, Lee YC, Jung SH, Li Z, Sui X, Wu J, Li X, Zhou T, Toth J, Baran A, Kori A, Chennamsetty SS, Safwan M, Alex V, Lyu X, Cheng L, Chu Q, Li P, Ji X, Zhang S, Shen Y, Dai L, Saha O, Sathish R, Melo T, Araújo T, Harangi B, Sheng B, Fang R, Sheet D, Hajdu A, Zheng Y, Mendonça AM, Zhang S, Campilho A, Zheng B, Shen D, Giancardo L, Quellec G, Mériaudeau F. IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge. Med Image Anal 2019; 59:101561. [PMID: 31671320 DOI: 10.1016/j.media.2019.101561] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 09/09/2019] [Accepted: 09/16/2019] [Indexed: 02/07/2023]
Abstract
Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is challenging due to the scarcity of medical professionals able to screen a growing global diabetic population at risk for DR. Computer-aided disease diagnosis in retinal image analysis could provide a sustainable approach for such large-scale screening effort. The recent scientific advances in computing capacity and machine learning approaches provide an avenue for biomedical scientists to reach this goal. Aiming to advance the state-of-the-art in automatic DR diagnosis, a grand challenge on "Diabetic Retinopathy - Segmentation and Grading" was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI - 2018). In this paper, we report the set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD). There were three principal sub-challenges: lesion segmentation, disease severity grading, and localization of retinal landmarks and segmentation. These multiple tasks in this challenge allow to test the generalizability of algorithms, and this is what makes it different from existing ones. It received a positive response from the scientific community with 148 submissions from 495 registrations effectively entered in this challenge. This paper outlines the challenge, its organization, the dataset used, evaluation methods and results of top-performing participating solutions. The top-performing approaches utilized a blend of clinical information, data augmentation, and an ensemble of models. These findings have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.
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Affiliation(s)
- Prasanna Porwal
- Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India; School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA.
| | - Samiksha Pachade
- Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India; School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA
| | - Manesh Kokare
- Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India
| | | | | | | | - Lihong Liu
- Ping An Technology (Shenzhen) Co.,Ltd, China
| | | | - Xinhui Liu
- Ping An Technology (Shenzhen) Co.,Ltd, China
| | | | - TianBo Wu
- Ping An Technology (Shenzhen) Co.,Ltd, China
| | - Jing Xiao
- Ping An Technology (Shenzhen) Co.,Ltd, China
| | | | | | - Yunzhi Wang
- School of Electrical and Computer Engineering, University of Oklahoma, USA
| | - Gopichandh Danala
- School of Electrical and Computer Engineering, University of Oklahoma, USA
| | - Linsheng He
- School of Electrical and Computer Engineering, University of Oklahoma, USA
| | - Yoon Ho Choi
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Yeong Chan Lee
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Sang-Hyuk Jung
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Zhongyu Li
- Department of Computer Science, University of North Carolina at Charlotte, USA
| | - Xiaodan Sui
- School of Information Science and Engineering, Shandong Normal University, China
| | - Junyan Wu
- Cleerly Inc., New York, United States
| | | | - Ting Zhou
- University at Buffalo, New York, United States
| | - Janos Toth
- University of Debrecen, Faculty of Informatics 4002 Debrecen, POB 400, Hungary
| | - Agnes Baran
- University of Debrecen, Faculty of Informatics 4002 Debrecen, POB 400, Hungary
| | | | | | | | | | - Xingzheng Lyu
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China; Machine Learning for Bioimage Analysis Group, Bioinformatics Institute, A*STAR, Singapore
| | - Li Cheng
- Machine Learning for Bioimage Analysis Group, Bioinformatics Institute, A*STAR, Singapore; Department of Electric and Computer Engineering, University of Alberta, Canada
| | - Qinhao Chu
- School of Computing, National University of Singapore, Singapore
| | - Pengcheng Li
- School of Computing, National University of Singapore, Singapore
| | - Xin Ji
- Beijing Shanggong Medical Technology Co., Ltd., China
| | - Sanyuan Zhang
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Yaxin Shen
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China
| | - Ling Dai
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China
| | | | | | - Tânia Melo
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
| | - Teresa Araújo
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal; FEUP - Faculty of Engineering of the University of Porto, Porto, Portugal
| | - Balazs Harangi
- University of Debrecen, Faculty of Informatics 4002 Debrecen, POB 400, Hungary
| | - Bin Sheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China
| | - Ruogu Fang
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, USA
| | | | - Andras Hajdu
- University of Debrecen, Faculty of Informatics 4002 Debrecen, POB 400, Hungary
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, China
| | - Ana Maria Mendonça
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal; FEUP - Faculty of Engineering of the University of Porto, Porto, Portugal
| | - Shaoting Zhang
- Department of Computer Science, University of North Carolina at Charlotte, USA
| | - Aurélio Campilho
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal; FEUP - Faculty of Engineering of the University of Porto, Porto, Portugal
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Luca Giancardo
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA
| | | | - Fabrice Mériaudeau
- Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Malaysia; ImViA/IFTIM, Université de Bourgogne, Dijon, France
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Deep Ensemble Learning Based Objective Grading of Macular Edema by Extracting Clinically Significant Findings from Fused Retinal Imaging Modalities. SENSORS 2019; 19:s19132970. [PMID: 31284442 PMCID: PMC6651513 DOI: 10.3390/s19132970] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2019] [Revised: 06/21/2019] [Accepted: 06/26/2019] [Indexed: 12/22/2022]
Abstract
Macular edema (ME) is a retinal condition in which central vision of a patient is affected. ME leads to accumulation of fluid in the surrounding macular region resulting in a swollen macula. Optical coherence tomography (OCT) and the fundus photography are the two widely used retinal examination techniques that can effectively detect ME. Many researchers have utilized retinal fundus and OCT imaging for detecting ME. However, to the best of our knowledge, no work is found in the literature that fuses the findings from both retinal imaging modalities for the effective and more reliable diagnosis of ME. In this paper, we proposed an automated framework for the classification of ME and healthy eyes using retinal fundus and OCT scans. The proposed framework is based on deep ensemble learning where the input fundus and OCT scans are recognized through the deep convolutional neural network (CNN) and are processed accordingly. The processed scans are further passed to the second layer of the deep CNN model, which extracts the required feature descriptors from both images. The extracted descriptors are then concatenated together and are passed to the supervised hybrid classifier made through the ensemble of the artificial neural networks, support vector machines and naïve Bayes. The proposed framework has been trained on 73,791 retinal scans and is validated on 5100 scans of publicly available Zhang dataset and Rabbani dataset. The proposed framework achieved the accuracy of 94.33% for diagnosing ME and healthy subjects and achieved the mean dice coefficient of 0.9019 ± 0.04 for accurately extracting the retinal fluids, 0.7069 ± 0.11 for accurately extracting hard exudates and 0.8203 ± 0.03 for accurately extracting retinal blood vessels against the clinical markings.
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18
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Randive SN, Senapati RK, Rahulkar AD. A review on computer-aided recent developments for automatic detection of diabetic retinopathy. J Med Eng Technol 2019; 43:87-99. [PMID: 31198073 DOI: 10.1080/03091902.2019.1576790] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Diabetic retinopathy is a serious microvascular disorder that might result in loss of vision and blindness. It seriously damages the retinal blood vessels and reduces the light-sensitive inner layer of the eye. Due to the manual inspection of retinal fundus images on diabetic retinopathy to detect the morphological abnormalities in Microaneurysms (MAs), Exudates (EXs), Haemorrhages (HMs), and Inter retinal microvascular abnormalities (IRMA) is very difficult and time consuming process. In order to avoid this, the regular follow-up screening process, and early automatic Diabetic Retinopathy detection are necessary. This paper discusses various methods of analysing automatic retinopathy detection and classification of different grading based on the severity levels. In addition, retinal blood vessel detection techniques are also discussed for the ultimate detection and diagnostic procedure of proliferative diabetic retinopathy. Furthermore, the paper elaborately discussed the systematic review accessed by authors on various publicly available databases collected from different medical sources. In the survey, meta-analysis of several methods for diabetic feature extraction, segmentation and various types of classifiers have been used to evaluate the system performance metrics for the diagnosis of DR. This survey will be helpful for the technical persons and researchers who want to focus on enhancing the diagnosis of a system that would be more powerful in real life.
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Affiliation(s)
- Santosh Nagnath Randive
- a Department of Electronics & Communication Engineering , Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram , Guntur , Andhra Pradesh , India
| | - Ranjan K Senapati
- a Department of Electronics & Communication Engineering , Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram , Guntur , Andhra Pradesh , India
| | - Amol D Rahulkar
- b Department of Electrical and Electronics Engineering , National Institute of Technology , Goa , India
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19
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Enhanced CAE system for detection of exudates and diagnosis of diabetic retinopathy stages in fundus retinal images using soft computing techniques. POLISH JOURNAL OF MEDICAL PHYSICS AND ENGINEERING 2019. [DOI: 10.2478/pjmpe-2019-0018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Abstract
Diabetic Retinopathy (DR) is one of the leading causes of visual impairment. Diabetic Retinopathy is the most recent technique of identifying the intensity of acid secretion in the eye for diabetic patients. The identification of DR is performed by visual analysis of retinal images for exudates (fat deposits) and the main patterns are traced by ophthalmologists. This paper proposes a fully automated Computer Assisted Evaluation (CAE) system which comprises of a set of algorithms for exudates detection and to classify the different stages of Diabetics Retinopathy, which are identified as either normal or mild or moderate or severe. Experimental validation is performed on a real fundus retinal image database. The segmentation of exudates is achieved using fuzzy C-means clustering and entropy filtering. An optimal set obtained from the statistical textural features (GLCM and GLHM) is extracted from the segmented exudates for classifying the different stages of Diabetics Retinopathy. The different stages of Diabetic Retinopathy are classified using three classifiers such as Back Propagation Neural Network (BPN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM). The experimental results show that the SVM classifiers outperformed other classifiers for the examined fundus retinal image dataset. The results obtained confirm that with new a set of texture features, the proposed methodology provides better performance when compared to the other methods available in the literature. These results suggest that our proposed method in this paper can be useful as a diagnostic aid system for Diabetic Retinopathy.
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20
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Romero-Oraá R, Jiménez-García J, García M, López-Gálvez MI, Oraá-Pérez J, Hornero R. Entropy Rate Superpixel Classification for Automatic Red Lesion Detection in Fundus Images. ENTROPY 2019; 21:e21040417. [PMID: 33267131 PMCID: PMC7514906 DOI: 10.3390/e21040417] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 04/17/2019] [Accepted: 04/17/2019] [Indexed: 12/26/2022]
Abstract
Diabetic retinopathy (DR) is the main cause of blindness in the working-age population in developed countries. Digital color fundus images can be analyzed to detect lesions for large-scale screening. Thereby, automated systems can be helpful in the diagnosis of this disease. The aim of this study was to develop a method to automatically detect red lesions (RLs) in retinal images, including hemorrhages and microaneurysms. These signs are the earliest indicators of DR. Firstly, we performed a novel preprocessing stage to normalize the inter-image and intra-image appearance and enhance the retinal structures. Secondly, the Entropy Rate Superpixel method was used to segment the potential RL candidates. Then, we reduced superpixel candidates by combining inaccurately fragmented regions within structures. Finally, we classified the superpixels using a multilayer perceptron neural network. The used database contained 564 fundus images. The DB was randomly divided into a training set and a test set. Results on the test set were measured using two different criteria. With a pixel-based criterion, we obtained a sensitivity of 81.43% and a positive predictive value of 86.59%. Using an image-based criterion, we reached 84.04% sensitivity, 85.00% specificity and 84.45% accuracy. The algorithm was also evaluated on the DiaretDB1 database. The proposed method could help specialists in the detection of RLs in diabetic patients.
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Affiliation(s)
- Roberto Romero-Oraá
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain
- Correspondence: ; Tel.: +34-983-425-589
| | - Jorge Jiménez-García
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain
| | - María García
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain
| | - María I. López-Gálvez
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain
- Department of Ophthalmology, Hospital Clínico Universitario de Valladolid, 47003 Valladolid, Spain
- Instituto de Oftalmobiología Aplicada (IOBA), University of Valladolid, 47011 Valladolid, Spain
| | - Javier Oraá-Pérez
- Department of Ophthalmology, Hospital Clínico Universitario de Valladolid, 47003 Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain
- Instituto de Investigación en Matemáticas (IMUVA), University of Valladolid, 47011 Valladolid, Spain
- Instituto de Neurociencias de Castilla y León (INCYL), University of Salamanca, 37007 Salamanca, Spain
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21
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Daien V, Muyl-Cipollina A. [Can Big Data change our practices?]. J Fr Ophtalmol 2019; 42:551-571. [PMID: 30979558 DOI: 10.1016/j.jfo.2018.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 11/22/2018] [Indexed: 11/19/2022]
Abstract
The European Medicines Agency has defined Big Data by the "3 V's": Volume, Velocity and Variety. These large databases allow access to real life data on patient care. They are particularly suited for studies of adverse events and pharmacoepidemiology. Deep learning is a collection of algorithms used in machine learning, used to model high-level abstractions in data using model architectures, which are composed of multiple nonlinear transformations. This article shows how Big Data and Deep Learning can help in ophthalmology, pointing out their advantages and disadvantages. A literature review is presented in this article illustrating the uses of Deep Learning in ophthalmology.
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Affiliation(s)
- V Daien
- Service d'ophtalmologique, hôpital Gui De Chauliac, 80, avenue Augustin Fliche, 34295 Montpellier, France; Inserm, epidemiological and clinical research, université Montpellier, 34295 Montpellier, France; The Save Sight Institute, Sydney Medical School, The University of Sydney, Sydney, Australie
| | - A Muyl-Cipollina
- Service d'ophtalmologique, hôpital Gui De Chauliac, 80, avenue Augustin Fliche, 34295 Montpellier, France.
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22
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23
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Chanda K, Issac A, Dutta MK. An Adaptive Algorithm for Detection of Exudates Based on Localized Properties of Fundus Images. INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS 2019. [DOI: 10.4018/ijehmc.2019010102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This article presents an algorithm to detect exudates, which can be considered as one of the many abnormalities, to identify diabetic retinopathy from fundus images. The algorithm is invariant to illumination and works well on poor contrast images with high reflection noise. The artefacts are correctly rejected despite their colour, intensity and contrast being almost similar to that of exudates. Optic disc is localized and segmented using average filter of specially determined size which is an important step in the rejection of false positives. Exudates are located by generating candidate regions using variance and median filters followed by morphological reconstruction. The strategic selection of local properties to decide the threshold, makes this approach novel and adaptive, that is highly accurate for detection of exudates. The proposed method was tested on two publicly available labelled databases (DIARETDB1 and MESSIDOR) and a database from a local hospital and achieved a sensitivity of 96.765% and a positive predictive value of 93.514%.
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Affiliation(s)
- Katha Chanda
- College of Computing, Georgia Institute of Technology, Atlanta, USA
| | - Ashish Issac
- Department of Electronics & Communication Engineering, Amity University, Noida, India
| | - Malay Kishore Dutta
- Center for Advanced Studies, Dr. A.P.J Abdul Kalam Technical University, Lucknow, India
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24
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Amin J, Sharif M, Rehman A, Raza M, Mufti MR. Diabetic retinopathy detection and classification using hybrid feature set. Microsc Res Tech 2018; 81:990-996. [DOI: 10.1002/jemt.23063] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Revised: 04/25/2018] [Accepted: 05/15/2018] [Indexed: 12/25/2022]
Affiliation(s)
- Javeria Amin
- Department of Computer ScienceUniversity of WahPakistan
| | - Muhammad Sharif
- Department of Computer ScienceCOMSATS University Islamabad Wah Campus Pakistan
| | - Amjad Rehman
- College of Computer and Information Systems, Al‐Yamamah University Riyadh 11512 Saudi Arabia
| | - Mudassar Raza
- Department of Computer ScienceCOMSATS University Islamabad Wah Campus Pakistan
| | - Muhammad Rafiq Mufti
- Department of Computer ScienceCOMSATS Institute of Information Technology Vehari Pakistan
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25
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Randive SN, Rahulkar AD, Senapati RK. LVP extraction and triplet-based segmentation for diabetic retinopathy recognition. EVOLUTIONARY INTELLIGENCE 2018. [DOI: 10.1007/s12065-018-0158-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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26
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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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27
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Kusakunniran W, Wu Q, Ritthipravat P, Zhang J. Hard exudates segmentation based on learned initial seeds and iterative graph cut. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 158:173-183. [PMID: 29544783 DOI: 10.1016/j.cmpb.2018.02.011] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 02/02/2018] [Accepted: 02/16/2018] [Indexed: 06/08/2023]
Abstract
(Background and Objective): The occurrence of hard exudates is one of the early signs of diabetic retinopathy which is one of the leading causes of the blindness. Many patients with diabetic retinopathy lose their vision because of the late detection of the disease. Thus, this paper is to propose a novel method of hard exudates segmentation in retinal images in an automatic way. (Methods): The existing methods are based on either supervised or unsupervised learning techniques. In addition, the learned segmentation models may often cause miss-detection and/or fault-detection of hard exudates, due to the lack of rich characteristics, the intra-variations, and the similarity with other components in the retinal image. Thus, in this paper, the supervised learning based on the multilayer perceptron (MLP) is only used to identify initial seeds with high confidences to be hard exudates. Then, the segmentation is finalized by unsupervised learning based on the iterative graph cut (GC) using clusters of initial seeds. Also, in order to reduce color intra-variations of hard exudates in different retinal images, the color transfer (CT) is applied to normalize their color information, in the pre-processing step. (Results): The experiments and comparisons with the other existing methods are based on the two well-known datasets, e_ophtha EX and DIARETDB1. It can be seen that the proposed method outperforms the other existing methods in the literature, with the sensitivity in the pixel-level of 0.891 for the DIARETDB1 dataset and 0.564 for the e_ophtha EX dataset. The cross datasets validation where the training process is performed on one dataset and the testing process is performed on another dataset is also evaluated in this paper, in order to illustrate the robustness of the proposed method. (Conclusions): This newly proposed method integrates the supervised learning and unsupervised learning based techniques. It achieves the improved performance, when compared with the existing methods in the literature. The robustness of the proposed method for the scenario of cross datasets could enhance its practical usage. That is, the trained model could be more practical for unseen data in the real-world situation, especially when the capturing environments of training and testing images are not the same.
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Affiliation(s)
- Worapan Kusakunniran
- Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand.
| | - Qiang Wu
- School of Computing and Communications, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia.
| | - Panrasee Ritthipravat
- Department of Biomedical Engineering, Faculty of Engineer, Mahidol University, Nakhon Pathom, Thailand.
| | - Jian Zhang
- School of Computing and Communications, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia.
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28
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Automatic computer vision-based detection and quantitative analysis of indicative parameters for grading of diabetic retinopathy. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3443-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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29
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Intelligent Image Processing System for Detection and Segmentation of Regions of Interest in Retinal Images. Symmetry (Basel) 2018. [DOI: 10.3390/sym10030073] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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30
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Joshi S, Karule P. A review on exudates detection methods for diabetic retinopathy. Biomed Pharmacother 2018; 97:1454-1460. [DOI: 10.1016/j.biopha.2017.11.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 10/29/2017] [Accepted: 11/03/2017] [Indexed: 10/18/2022] Open
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31
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Kaur J, Mittal D. Estimation of severity level of non-proliferative diabetic retinopathy for clinical aid. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2018.05.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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32
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33
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Soomro TA, Gao J, Khan T, Hani AFM, Khan MAU, Paul M. Computerised approaches for the detection of diabetic retinopathy using retinal fundus images: a survey. Pattern Anal Appl 2017. [DOI: 10.1007/s10044-017-0630-y] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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34
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Mansour RF. Evolutionary Computing Enriched Computer-Aided Diagnosis System for Diabetic Retinopathy: A Survey. IEEE Rev Biomed Eng 2017; 10:334-349. [PMID: 28534786 DOI: 10.1109/rbme.2017.2705064] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Complications caused due to diabetes mellitus result in significant microvasculature that eventually causes diabetic retinopathy (DR) that keeps on increasing with time, and eventually causes complete vision loss. Identifying subtle variations in morphological changes in retinal blood vessels, optic disk, exudates, microaneurysms, hemorrhage, etc., is complicated and requires a robust computer-aided diagnosis (CAD) system so as to enable earlier and efficient DR diagnosis practices. In the majority of the existing CAD systems, functional enhancements have been realized time and again to ensure accurate and efficient diagnosis of DR. In this survey paper, a number of existing literature presenting DR CAD systems are discussed and analyzed. Both traditional and varoius evolutionary approaches, including genetic algorithm, particle swarm optimization, ant colony optimization, bee colony optimization, etc., based DR CAD have also been studied and their respective efficiencies have been discussed. Our survey revealed that evolutionary computing methods can play a vital role for optimizing DR-CAD functional components, such as proprocessing by enhancing filters coefficient, segmentation by enriching clustering, feature extraction, feature selection, and dimensional reduction, as well as classification.
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Wisaeng K, Sa-ngiamvibool W. Improved fuzzy C-means clustering in the process of exudates detection using mathematical morphology. Soft comput 2017. [DOI: 10.1007/s00500-017-2532-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Valverde C, Garcia M, Hornero R, Lopez-Galvez MI. Automated detection of diabetic retinopathy in retinal images. Indian J Ophthalmol 2016; 64:26-32. [PMID: 26953020 PMCID: PMC4821117 DOI: 10.4103/0301-4738.178140] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Diabetic retinopathy (DR) is a disease with an increasing prevalence and the main cause of blindness among working-age population. The risk of severe vision loss can be significantly reduced by timely diagnosis and treatment. Systematic screening for DR has been identified as a cost-effective way to save health services resources. Automatic retinal image analysis is emerging as an important screening tool for early DR detection, which can reduce the workload associated to manual grading as well as save diagnosis costs and time. Many research efforts in the last years have been devoted to developing automatic tools to help in the detection and evaluation of DR lesions. However, there is a large variability in the databases and evaluation criteria used in the literature, which hampers a direct comparison of the different studies. This work is aimed at summarizing the results of the available algorithms for the detection and classification of DR pathology. A detailed literature search was conducted using PubMed. Selected relevant studies in the last 10 years were scrutinized and included in the review. Furthermore, we will try to give an overview of the available commercial software for automatic retinal image analysis.
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Affiliation(s)
- Carmen Valverde
- Department of Ophthalmology, Hospital de Medina del Campo, Medina del Campo, Valladolid, Spain
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Maity M, Das DK, Dhane DM, Chakraborty C, Maiti A. Fusion of Entropy-Based Thresholding and Active Contour Model for Detection of Exudate and Optic Disc in Color Fundus Images. J Med Biol Eng 2016. [DOI: 10.1007/s40846-016-0193-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Imani E, Pourreza HR. A novel method for retinal exudate segmentation using signal separation algorithm. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 133:195-205. [PMID: 27393810 DOI: 10.1016/j.cmpb.2016.05.016] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2015] [Revised: 04/24/2016] [Accepted: 05/27/2016] [Indexed: 06/06/2023]
Abstract
Diabetic retinopathy is one of the major causes of blindness in the world. Early diagnosis of this disease is vital to the prevention of visual loss. The analysis of retinal lesions such as exudates, microaneurysms and hemorrhages is a prerequisite to detect diabetic disorders such as diabetic retinopathy and macular edema in fundus images. This paper presents an automatic method for the detection of retinal exudates. The novelty of this method lies in the use of Morphological Component Analysis (MCA) algorithm to separate lesions from normal retinal structures to facilitate the detection process. In the first stage, vessels are separated from lesions using the MCA algorithm with appropriate dictionaries. Then, the lesion part of retinal image is prepared for the detection of exudate regions. The final exudate map is created using dynamic thresholding and mathematical morphologies. Performance of the proposed method is measured on the three publicly available DiaretDB, HEI-MED and e-ophtha datasets. Accordingly, the AUC of 0.961 and 0.948 and 0.937 is achieved respectively, which are greater than most of the state-of-the-art methods.
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Affiliation(s)
- Elaheh Imani
- Machine Vision Lab., Ferdowsi University of Mashhad, Mashhad, Iran
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Jaya T, Dheeba J, Singh NA. Detection of Hard Exudates in Colour Fundus Images Using Fuzzy Support Vector Machine-Based Expert System. J Digit Imaging 2016; 28:761-8. [PMID: 25822397 DOI: 10.1007/s10278-015-9793-5] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Diabetic retinopathy is a major cause of vision loss in diabetic patients. Currently, there is a need for making decisions using intelligent computer algorithms when screening a large volume of data. This paper presents an expert decision-making system designed using a fuzzy support vector machine (FSVM) classifier to detect hard exudates in fundus images. The optic discs in the colour fundus images are segmented to avoid false alarms using morphological operations and based on circular Hough transform. To discriminate between the exudates and the non-exudates pixels, colour and texture features are extracted from the images. These features are given as input to the FSVM classifier. The classifier analysed 200 retinal images collected from diabetic retinopathy screening programmes. The tests made on the retinal images show that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and features sets, the area under the receiver operating characteristic curve reached 0.9606, which corresponds to a sensitivity of 94.1% with a specificity of 90.0%. The results suggest that detecting hard exudates using FSVM contribute to computer-assisted detection of diabetic retinopathy and as a decision support system for ophthalmologists.
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Affiliation(s)
- T Jaya
- Department of Electronics and Communication Engineering, CSI Institute of Technology, Nagercoil, India.
| | - J Dheeba
- Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Thuckalay, India, 629180.
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Partovi M, Rasta SH, Javadzadeh A. Automatic detection of retinal exudates in fundus images of diabetic retinopathy patients. JOURNAL OF ANALYTICAL RESEARCH IN CLINICAL MEDICINE 2016. [DOI: 10.15171/jarcm.2016.017] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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Rasta SH, Nikfarjam S, Javadzadeh A. Detection of retinal capillary nonperfusion in fundus fluorescein angiogram of diabetic retinopathy. ACTA ACUST UNITED AC 2015; 5:183-90. [PMID: 26929922 PMCID: PMC4769788 DOI: 10.15171/bi.2015.27] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2015] [Revised: 12/19/2015] [Accepted: 12/26/2015] [Indexed: 11/09/2022]
Abstract
INTRODUCTION Retinal capillary nonperfusion (CNP) is one of the retinal vascular diseases in diabetic retinopathy (DR) patients. As there is no comprehensive detection technique to recognize CNP areas, we proposed a different method for computing detection of ischemic retina, non-perfused (NP) regions, in fundus fluorescein angiogram (FFA) images. METHODS Whilst major vessels appear as ridges, non-perfused areas are usually observed as ponds that are surrounded by healthy capillaries in FFA images. A new technique using homomorphic filtering to correct light illumination and detect the ponds surrounded in healthy capillaries on FFA images was designed and applied on DR fundus images. These images were acquired from the diabetic patients who had referred to the Nikookari hospital and were diagnosed for diabetic retinopathy during one year. Our strategy was screening the whole image with a fixed window size, which is small enough to enclose areas with identified topographic characteristics. To discard false nominees, we also performed a thresholding operation on the screen and marked images. To validate its performance we applied our detection algorithm on 41 FFA diabetic retinopathy fundus images in which the CNP areas were manually delineated by three clinical experts. RESULTS Lesions were found as smooth regions with very high uniformity, low entropy, and small intensity variations in FFA images. The results of automated detection method were compared with manually marked CNP areas so achieved sensitivity of 81%, specificity of 78%, and accuracy of 91%.The result was present as a Receiver operating character (ROC) curve, which has an area under the curve (AUC) of 0.796 with 95% confidence intervals. CONCLUSION This technique introduced a new automated detection algorithm to recognize non-perfusion lesions on FFA. This has potential to assist detecting and managing of ischemic retina and may be incorporated into automated grading diabetic retinopathy structures.
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Affiliation(s)
- Seyed Hossein Rasta
- Department of Medical Bioengineering, Stem Cell Research Center, Tabriz University of Medical Sciences, Tabriz, Iran ; School of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Shima Nikfarjam
- Department of Medical Bioengineering, Stem Cell Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Alireza Javadzadeh
- Department of Ophthalmology, Tabriz University of Medical Sciences, Tabriz, Iran
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TASGAONKAR MADHURI, KHAMBETE MADHURI. INTEGRATING FUZZY C-MEANS AND MAHALANOBIS METRIC CLASSIFICATION FOR EXUDATE DETECTION IN COLOR FUNDUS IMAGING. J MECH MED BIOL 2015. [DOI: 10.1142/s0219519415500852] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Diabetes affects retinal structure of a diabetic patient by generating various lesions. Early detection of these lesions can avoid the loss of vision. Automation of detection process can be made easily feasible to masses by the use of fundus imaging. Detection of exudates is significant in diabetic retinopathy (DR) as they are earlier signs and can cause blindness. Finding the exact location as well as correct number of exudates play vital role in the overall treatment of a patient. This paper presents an algorithm for automatic detection of exudates for DR. The algorithm combines the advantages of supervised and unsupervised techniques. It uses fuzzy-C means (FCM) segmentation on coarse level and mahalanobis metric for finer classification of segmented pixels. Mahalanobis criterion gives significance to most relevant features and thus proves a better classifier. The results are validated using DIARETDB0 and DIARETDB1 databases and the ground truth provided with it. This evaluation provided 95.77% detection accuracy.
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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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Figueiredo IN, Kumar S, Oliveira CM, Ramos JD, Engquist B. Automated lesion detectors in retinal fundus images. Comput Biol Med 2015; 66:47-65. [PMID: 26378502 DOI: 10.1016/j.compbiomed.2015.08.008] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2015] [Revised: 07/15/2015] [Accepted: 08/08/2015] [Indexed: 11/17/2022]
Abstract
Diabetic retinopathy (DR) is a sight-threatening condition occurring in persons with diabetes, which causes progressive damage to the retina. The early detection and diagnosis of DR is vital for saving the vision of diabetic persons. The early signs of DR which appear on the surface of the retina are the dark lesions such as microaneurysms (MAs) and hemorrhages (HEMs), and bright lesions (BLs) such as exudates. In this paper, we propose a novel automated system for the detection and diagnosis of these retinal lesions by processing retinal fundus images. We devise appropriate binary classifiers for these three different types of lesions. Some novel contextual/numerical features are derived, for each lesion type, depending on its inherent properties. This is performed by analysing several wavelet bands (resulting from the isotropic undecimated wavelet transform decomposition of the retinal image green channel) and by using an appropriate combination of Hessian multiscale analysis, variational segmentation and cartoon+texture decomposition. The proposed methodology has been validated on several medical datasets, with a total of 45,770 images, using standard performance measures such as sensitivity and specificity. The individual performance, per frame, of the MA detector is 93% sensitivity and 89% specificity, of the HEM detector is 86% sensitivity and 90% specificity, and of the BL detector is 90% sensitivity and 97% specificity. Regarding the collective performance of these binary detectors, as an automated screening system for DR (meaning that a patient is considered to have DR if it is a positive patient for at least one of the detectors) it achieves an average 95-100% of sensitivity and 70% of specificity at a per patient basis. Furthermore, evaluation conducted on publicly available datasets, for comparison with other existing techniques, shows the promising potential of the proposed detectors.
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Affiliation(s)
- I N Figueiredo
- CMUC, Department of Mathematics, University of Coimbra, Portugal.
| | - S Kumar
- Department of Applied Sciences, National Institute of Technology Delhi, Delhi 110040, India
| | - C M Oliveira
- Retmarker, Coimbra, Portugal; Universidade Nova de Lisboa, Portugal
| | | | - B Engquist
- Department of Mathematics and ICES, The University of Texas at Austin, Austin, USA
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MOOKIAH MUTHURAMAKRISHNAN, TAN JENHONG, CHUA CHUAKUANG, NG EYK, LAUDE AUGUSTINUS, TONG LOUIS. AUTOMATED CHARACTERIZATION AND DETECTION OF DIABETIC RETINOPATHY USING TEXTURE MEASURES. J MECH MED BIOL 2015. [DOI: 10.1142/s0219519415500451] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The chronic and uncontrolled diabetes mellitus (DM) damages the retinal blood vessels leading to diabetic retinopathy (DR). The advanced stage of DR leads to loss of vision and subsequently blindness. The morphological changes during the progression of DR can be diagnosed using digital fundus images. The pathological changes in the retina influence the variations in pixel patterns which can be quantified using texture measures. In this paper, we have explored different texture measures namely statistical moments, gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), local binary pattern (LBP), laws mask energy (LME), fractal dimension (FD), fourier spectrum (FS) and Gabor wavelet to characterize and classify the normal and DR classes. We have tabulated 109 texture parameters for the normal and DR classes. Further, these features were subjected to empirical receiver operating characteristic (ROC) based ranking to select optimal feature set. The ranked nested features were fed to the support vector machine (SVM) classifier with different kernel functions to evaluate the highest performance measure using the least number of features to discriminate normal and DR classes. Our proposed system was evaluated using two different databases Kasturba Medical College Hospital (KMCH) and Tan Tock Seng Hospital (TTSH), each with 340 images (170 normal and 170 DR). We have also formulated an integrated index called as diabetic retinopathy risk index (DRRI) using selected texture features to discriminate normal and DR classes using single number. The proposed frame work can be used to help the clinicians and also for mass DR screening programs.
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Affiliation(s)
| | - JEN HONG TAN
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - CHUA KUANG CHUA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - E. Y. K. NG
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - AUGUSTINUS LAUDE
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - LOUIS TONG
- Singapore National Eye Center, Singapore 168751, Singapore
- Ocular Surface Research Group, Singapore Eye Research Institute, Singapore 168751, Singapore
- Duke-NUS Graduate Medical School, Singapore 169857, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
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Sundaresan V, Ram K, Joshi N, Sivaprakasam M, Gandhi R. Computer-assisted grading of diabetic macular edema on retinal color fundus images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2015:4330-4333. [PMID: 26737253 DOI: 10.1109/embc.2015.7319353] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Diabetic macular edema (DME) is one of the vision-impairing manifestations of Diabetic Retinopathy (DR). Early detection and treatment of DME can prevent permanent vision loss in people suffering from DR. However, the clinical detection through biomicroscopy is time-consuming. In this paper, a computer-assisted grading method has been proposed to determine the DME severity based on the spatial distribution of exudative lesions around macula. The region around macula is classified into zonal levels and severity of the DME is graded based on the presence of exudative lesions in each zone. The proposed method has been evaluated on diverse public and local databases, and produced the sensitivity of 89.54% for 9.1 false positive per image (FPPI) for exudate detection and 98.8% accuracy for DME grading.
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MAHENDRAN G, DHANASEKARAN R. DETECTION AND LOCALIZATION OF RETINAL EXUDATES FOR DIABETIC RETINOPATHY. J BIOL SYST 2015. [DOI: 10.1142/s0218339015500102] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Diabetic retinopathy (DR) is a complication of diabetes caused by changes in the blood vessels of the retina. Initially, the DR causes trivial changes in the retinal capillary. The symptoms can blur or distort patients' vision, which are the main causes of blindness. The DR is characterized by the presence of exudates at the nonproliferative stage. Once damaged by DR, the effects will be permanent and hence an earlier treatment is considered as vital. The presence of exudates is detected by ophthalmologists from the dilated retinal images, which are captured by dropping chemical solution into the patient's eye that leads to irritation. Therefore, there is a need for an alternative method toward the detection of exudates using image processing algorithms from the nondilated images. In this paper, an automated method is proposed for the detection of exudates using the fuzzy C-Means (FCM) clustering technique and reconstruction through a superimposition process in the absence of dilating patient's eye. The segmented result of FCM is compared with the result obtained using the Fuzzy K-Means segmentation algorithm. The sensitivity and specificity values for the exudates detection using the FCM algorithm are 87.38% and 96.94%, respectively. On the other hand, sensitivity and specificity values for the exudates detection using the K-Means algorithm are 75.04% and 93.73%, respectively.
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Affiliation(s)
- G. MAHENDRAN
- Department of Electronics and Communication Engineering, Syed Ammal Engineering College, Ramanathapuram, Tamilnadu, India
| | - R. DHANASEKARAN
- Department of Electrical and Electronics Engineering, Syed Ammal Engineering College, Ramanathapuram, Tamilnadu, India
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Pereira C, Gonçalves L, Ferreira M. Exudate segmentation in fundus images using an ant colony optimization approach. Inf Sci (N Y) 2015. [DOI: 10.1016/j.ins.2014.10.059] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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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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