1
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Mahapatra S, Agrawal S, Mishro PK, Panda R, Dora L, Pachori RB. A Review on Retinal Blood Vessel Enhancement and Segmentation Techniques for Color Fundus Photography. Crit Rev Biomed Eng 2024; 52:41-69. [PMID: 37938183 DOI: 10.1615/critrevbiomedeng.2023049348] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
The retinal image is a trusted modality in biomedical image-based diagnosis of many ophthalmologic and cardiovascular diseases. Periodic examination of the retina can help in spotting these abnormalities in the early stage. However, to deal with today's large population, computerized retinal image analysis is preferred over manual inspection. The precise extraction of the retinal vessel is the first and decisive step for clinical applications. Every year, many more articles are added to the literature that describe new algorithms for the problem at hand. The majority of the review article is restricted to a fairly small number of approaches, assessment indices, and databases. In this context, a comprehensive review of different vessel extraction methods is inevitable. It includes the development of a first-hand classification of these methods. A bibliometric analysis of these articles is also presented. The benefits and drawbacks of the most commonly used techniques are summarized. The primary challenges, as well as the scope of possible changes, are discussed. In order to make a fair comparison, numerous assessment indices are considered. The findings of this survey could provide a new path for researchers for further work in this domain.
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Affiliation(s)
- Sakambhari Mahapatra
- Department of Electronics and Telecommunication Engineering, Veer Surendra Sai University of Technology, Burla, India
| | - Sanjay Agrawal
- Department of Electronics and Telecommunication Engineering, Veer Surendra Sai University of Technology, Burla, India
| | - Pranaba K Mishro
- Department of Electronics and Telecommunication Engineering, Veer Surendra Sai University of Technology, Burla, India
| | - Rutuparna Panda
- Department of Electronics and Telecommunication Engineering, Veer Surendra Sai University of Technology, Burla, India
| | - Lingraj Dora
- Department of Electrical and Electronics Engineering, Veer Surendra Sai University of Technology, Burla, India
| | - Ram Bilas Pachori
- Department of Electrical Engineering, Indian Institute of Technology Indore, Indore, India
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2
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Kv R, Prasad K, Peralam Yegneswaran P. Segmentation and Classification Approaches of Clinically Relevant Curvilinear Structures: A Review. J Med Syst 2023; 47:40. [PMID: 36971852 PMCID: PMC10042761 DOI: 10.1007/s10916-023-01927-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 02/25/2023] [Indexed: 03/29/2023]
Abstract
Detection of curvilinear structures from microscopic images, which help the clinicians to make an unambiguous diagnosis is assuming paramount importance in recent clinical practice. Appearance and size of dermatophytic hyphae, keratitic fungi, corneal and retinal vessels vary widely making their automated detection cumbersome. Automated deep learning methods, endowed with superior self-learning capacity, have superseded the traditional machine learning methods, especially in complex images with challenging background. Automatic feature learning ability using large input data with better generalization and recognition capability, but devoid of human interference and excessive pre-processing, is highly beneficial in the above context. Varied attempts have been made by researchers to overcome challenges such as thin vessels, bifurcations and obstructive lesions in retinal vessel detection as revealed through several publications reviewed here. Revelations of diabetic neuropathic complications such as tortuosity, changes in the density and angles of the corneal fibers have been successfully sorted in many publications reviewed here. Since artifacts complicate the images and affect the quality of analysis, methods addressing these challenges have been described. Traditional and deep learning methods, that have been adapted and published between 2015 and 2021 covering retinal vessels, corneal nerves and filamentous fungi have been summarized in this review. We find several novel and meritorious ideas and techniques being put to use in the case of retinal vessel segmentation and classification, which by way of cross-domain adaptation can be utilized in the case of corneal and filamentous fungi also, making suitable adaptations to the challenges to be addressed.
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Affiliation(s)
- Rajitha Kv
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India
| | - Keerthana Prasad
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India.
| | - Prakash Peralam Yegneswaran
- Department of Microbiology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India
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3
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Sidhu RK, Sachdeva J, Katoch D. Segmentation of retinal blood vessels by a novel hybrid technique- Principal Component Analysis (PCA) and Contrast Limited Adaptive Histogram Equalization (CLAHE). Microvasc Res 2023; 148:104477. [PMID: 36746364 DOI: 10.1016/j.mvr.2023.104477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 12/22/2022] [Accepted: 01/11/2023] [Indexed: 02/05/2023]
Abstract
Diabetic Retinopathy is a persistent disease of eyes that may lead to permanent loss of sight. In this paper, methodology is proposed to segment region of interest (ROI) i.e. new blood vessels in fundus images of retina of Diabetic Retinopathy (DR). The database of 50 fundus retinal images of healthy subjects and DR patients is fetched from Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India. The experimental set up consists of three set of experiments for the disease. For DR, in the first stage of automated blood vessel segmentation, gray-scale image is produced from the colored image using Principal Component Analysis (PCA) in the preprocessing step. The contrast enhancement by the Contrast Limited Adaptive Histogram Equalization (CLAHE) highlights the retinal blood vessels in the gray-scale image i.e. it unsheathed newly formed retinal blood vessels whereas PCA preserved their texture and color discrimination in DR images. The expert ophthalmologist(s) scrutiny on both internet repository and real time data acted as the gold standard for further analysis and formation of the proposed method. Further, ophthalmologists ascertained the forming of new blood vessels only on the disc region and divulging them, which were impossible with the naked eye. These operations help in extracting retinal blood vessels present on the disc and non-disc region of the image. The comparison of the results are done with the state of art methods like watershed transform. It is observed from the results that the new blood vessels are better segmented by the proposed methodology and are marked by the experienced ophthalmologist for validation. Further, for quantitative analysis, the features are extracted from new blood vessels as they are crucial for scientific interpretation. The results of the features lie in permissible limits such as no. of segments vary from 2 to 5 and length of segments varies from 49 to 164 pixels. Similarly, other features such as gray level of new blood vessels lie in 0.296-0.935 normalized range, coefficient with variations in gray level in the range of 0.658-10.10 and distance from vessel origin lie in the range of 56-82 pixels respectively. Both quantitative and qualitative results show that the methodologies proposed boosted the ophthalmic and clinical diagnosis. The developed method further handled the false detection of vessels near the optic disk boundary, under-segmentation of thin vessels, detection of pathological anomalies such as exudates, micro-aneurysms and cotton wool spots. From the numerical analysis, ophthalmologist extracted the information of number of vessels formed, length of the new vessels, observation that the new vessels appearing are less homogenous than the normal vessels. Also about the new vessels, whether they lie on the centre of disc region or towards its edges. These parameters lie as per the findings of the ophthalmologists on retinal images and automated detection helped in monitoring and comprehensive patient assessment. The experimental results show case that the proposed method has higher sensitivity, specificity and accuracy as compared to state of art methods i.e. 0.9023, 0.9610 and 0.9921, respectively. Similar results are obtained on retinal fundus images of PGIMER Chandigarh with sensitivity-0.9234, specificity-0.9955 and accuracy-0.9682.
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Affiliation(s)
- R K Sidhu
- Department of Electronics and Communication Engineering, Chandigarh University, Mohali, India.
| | - Jainy Sachdeva
- Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering & Technology, Patiala, India.
| | - D Katoch
- Department of Ophthalmology, Advanced Eye Centre, PGIMER, Chandigarh, India.
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Wisaeng K. Retinal Blood-Vessel Extraction Using Weighted Kernel Fuzzy C-Means Clustering and Dilation-Based Functions. Diagnostics (Basel) 2023; 13:diagnostics13030342. [PMID: 36766446 PMCID: PMC9914389 DOI: 10.3390/diagnostics13030342] [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: 11/18/2022] [Revised: 01/04/2023] [Accepted: 01/09/2023] [Indexed: 01/19/2023] Open
Abstract
Automated blood-vessel extraction is essential in diagnosing Diabetic Retinopathy (DR) and other eye-related diseases. However, the traditional methods for extracting blood vessels tend to provide low accuracy when dealing with difficult situations, such as extracting both micro and large blood vessels simultaneously with low-intensity images and blood vessels with DR. This paper proposes a complete preprocessing method to enhance original retinal images before transferring the enhanced images to a novel blood-vessel extraction method by a combined three extraction stages. The first stage focuses on the fast extraction of retinal blood vessels using Weighted Kernel Fuzzy C-Means (WKFCM) Clustering to draw the vessel feature from the retinal background. The second stage focuses on the accuracy of full-size images to achieve regional vessel feature recognition of large and micro blood vessels and to minimize false extraction. This stage implements the mathematical dilation operator from a trained model called Dilation-Based Function (DBF). Finally, an optimal parameter threshold is empirically determined in the third stage to remove non-vessel features in the binary image and improve the overall vessel extraction results. According to evaluations of the method via the datasets DRIVE, STARE, and DiaretDB0, the proposed WKFCM-DBF method achieved sensitivities, specificities, and accuracy performances of 98.12%, 98.20%, and 98.16%, 98.42%, 98.80%, and 98.51%, and 98.89%, 98.10%, and 98.09%, respectively.
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Affiliation(s)
- Kittipol Wisaeng
- Technology and Business Information System Unit, Mahasarakham Business School, Mahasarakham University, Mahasarakham 44150, Thailand
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5
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A Hybrid Method to Enhance Thick and Thin Vessels for Blood Vessel Segmentation. Diagnostics (Basel) 2021; 11:diagnostics11112017. [PMID: 34829365 PMCID: PMC8621384 DOI: 10.3390/diagnostics11112017] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/25/2021] [Accepted: 10/25/2021] [Indexed: 11/16/2022] Open
Abstract
Retinal blood vessels have been presented to contribute confirmation with regard to tortuosity, branching angles, or change in diameter as a result of ophthalmic disease. Although many enhancement filters are extensively utilized, the Jerman filter responds quite effectively at vessels, edges, and bifurcations and improves the visualization of structures. In contrast, curvelet transform is specifically designed to associate scale with orientation and can be used to recover from noisy data by curvelet shrinkage. This paper describes a method to improve the performance of curvelet transform further. A distinctive fusion of curvelet transform and the Jerman filter is presented for retinal blood vessel segmentation. Mean-C thresholding is employed for the segmentation purpose. The suggested method achieves average accuracies of 0.9600 and 0.9559 for DRIVE and CHASE_DB1, respectively. Simulation results establish a better performance and faster implementation of the suggested scheme in comparison with similar approaches seen in the literature.
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6
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Hao Y, Xie H, Qiu R. Construction and application of color fundus image segmentation algorithm based on Multi-Scale local combined global enhancement. Pak J Med Sci 2021; 37:1595-1599. [PMID: 34712289 PMCID: PMC8520383 DOI: 10.12669/pjms.37.6-wit.4848] [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: 04/03/2021] [Revised: 06/08/2021] [Accepted: 07/05/2021] [Indexed: 11/15/2022] Open
Abstract
Objective Aiming at the problem of low accuracy in extracting small blood vessels from existing retinal blood vessel images, a retinal blood vessel segmentation method based on a combination of a multi-scale linear detector and local and global enhancement is proposed. Methods The multi-scale line detector is studied, and it is divided into two parts: small scale and large scale. The small scale is used to detect the locally enhanced image and the large scale is used to detect the globally enhanced image. Fusion the response functions at different scales to get the final retinal vascular structure. Results Experiments on two databases STARE and DRIVE, show that the average vascular accuracy rates obtained by the algorithm reach 96.62% and 96.45%, and the average true positive rates reach 75.52% and 83.07%, respectively. Conclusion The segmentation accuracy is high, and better blood vessel segmentation results can be obtained.
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Affiliation(s)
- Yanjie Hao
- Yanjie Hao, Associate Chief Physician, Department of Ophthalmology, Jiaozhou Central Hospital of Qingdao, Qingdao, 266300, Shandong, China
| | - Hongbo Xie
- Hongbo Xie, Attending Physician, Department of Ophthalmology, Qingdao Women's and Children's Hospital, Qingdao, 266000, Shandong, China
| | - Rong Qiu
- Rong Qiu, Associate Chief Physician, Department of Ophthalmology, Zhucheng People's Hospital, Zhucheng, Shandong, China
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7
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Valizadeh A, Jafarzadeh Ghoushchi S, Ranjbarzadeh R, Pourasad Y. Presentation of a Segmentation Method for a Diabetic Retinopathy Patient's Fundus Region Detection Using a Convolutional Neural Network. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:7714351. [PMID: 34354746 PMCID: PMC8331281 DOI: 10.1155/2021/7714351] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 06/30/2021] [Accepted: 07/18/2021] [Indexed: 01/16/2023]
Abstract
Diabetic retinopathy is characteristic of a local distribution that involves early-stage risk factors and can forecast the evolution of the illness or morphological lesions related to the abnormality of retinal blood flows. Regional variations in retinal blood flow and modulation of retinal capillary width in the macular area and the retinal environment are also linked to the course of diabetic retinopathy. Despite the fact that diabetic retinopathy is frequent nowadays, it is hard to avoid. An ophthalmologist generally determines the seriousness of the retinopathy of the eye by directly examining color photos and evaluating them by visually inspecting the fundus. It is an expensive process because of the vast number of diabetic patients around the globe. We used the IDRiD data set that contains both typical diabetic retinopathic lesions and normal retinal structures. We provided a CNN architecture for the detection of the target region of 80 patients' fundus imagery. Results demonstrate that the approach described here can nearly detect 83.84% of target locations. This result can potentially be utilized to monitor and regulate patients.
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Affiliation(s)
- Amin Valizadeh
- Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Saeid Jafarzadeh Ghoushchi
- Department of Industrial Engineering, Urmia University of Technology (UUT), P.O. Box 57166-419, Urmia, Iran
| | - Ramin Ranjbarzadeh
- Department of Telecommunications Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran
| | - Yaghoub Pourasad
- Department of Electrical Engineering, Urmia University of Technology (UUT), P.O. Box 57166-419, Urmia, Iran
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8
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Ashraf MN, Hussain M, Habib Z. Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System. Curr Med Imaging 2021; 16:397-426. [PMID: 32410541 DOI: 10.2174/1573405615666190219102427] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 12/31/2018] [Accepted: 01/20/2019] [Indexed: 12/15/2022]
Abstract
Diabetic Retinopathy (DR) is a major cause of blindness in diabetic patients. The increasing population of diabetic patients and difficulty to diagnose it at an early stage are limiting the screening capabilities of manual diagnosis by ophthalmologists. Color fundus images are widely used to detect DR lesions due to their comfortable, cost-effective and non-invasive acquisition procedure. Computer Aided Diagnosis (CAD) of DR based on these images can assist ophthalmologists and help in saving many sight years of diabetic patients. In a CAD system, preprocessing is a crucial phase, which significantly affects its performance. Commonly used preprocessing operations are the enhancement of poor contrast, balancing the illumination imbalance due to the spherical shape of a retina, noise reduction, image resizing to support multi-resolution, color normalization, extraction of a field of view (FOV), etc. Also, the presence of blood vessels and optic discs makes the lesion detection more challenging because these two artifacts exhibit specific attributes, which are similar to those of DR lesions. Preprocessing operations can be broadly divided into three categories: 1) fixing the native defects, 2) segmentation of blood vessels, and 3) localization and segmentation of optic discs. This paper presents a review of the state-of-the-art preprocessing techniques related to three categories of operations, highlighting their significant aspects and limitations. The survey is concluded with the most effective preprocessing methods, which have been shown to improve the accuracy and efficiency of the CAD systems.
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Affiliation(s)
| | - Muhammad Hussain
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Zulfiqar Habib
- Department of Computer Science, COMSATS University Islamabad, Lahore, Pakistan
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9
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Dong J, Ai D, Fan J, Deng Q, Song H, Cheng Z, Liang P, Wang Y, Yang J. Local-global active contour model based on tensor-based representation for 3D ultrasound vessel segmentation. Phys Med Biol 2021; 66. [PMID: 33910173 DOI: 10.1088/1361-6560/abfc92] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 04/28/2021] [Indexed: 11/11/2022]
Abstract
Three-dimensional (3D) vessel segmentation can provide full spatial information about an anatomic structure to help physicians gain increased understanding of vascular structures, which plays an utmost role in many medical image-processing and analysis applications. The purpose of this paper aims to develop a 3D vessel-segmentation method that can improve segmentation accuracy in 3D ultrasound (US) images. We propose a 3D tensor-based active contour model method for accurate 3D vessel segmentation. With our method, the contrast-independent multiscale bottom-hat tensor representation and local-global information are captured. This strategy ensures the effective extraction of the boundaries of vessels from inhomogeneous and homogeneous regions without being affected by the noise and low-contrast of the 3D US images. Experimental results in clinical 3D US and public 3D Multiphoton Microscopy datasets are used for quantitative and qualitative comparison with several state-of-the-art vessel segmentation methods. Clinical experiments demonstrate that our method can achieve a smoother and more accurate boundary of the vessel object than competing methods. The mean SE, SP and ACC of the proposed method are: 0.7768 ± 0.0597, 0.9978 ± 0.0013 and 0.9971 ± 0.0015 respectively. Experiments on the public dataset show that our method can segment complex vessels in different medical images with noise and low- contrast.
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Affiliation(s)
- Jiahui Dong
- Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Danni Ai
- Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Jingfan Fan
- Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Qiaoling Deng
- Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Hong Song
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Zhigang Cheng
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing 100853, People's Republic of China
| | - Ping Liang
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing 100853, People's Republic of China
| | - Yongtian Wang
- Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Jian Yang
- Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
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10
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Mookiah MRK, Hogg S, MacGillivray TJ, Prathiba V, Pradeepa R, Mohan V, Anjana RM, Doney AS, Palmer CNA, Trucco E. A review of machine learning methods for retinal blood vessel segmentation and artery/vein classification. Med Image Anal 2020; 68:101905. [PMID: 33385700 DOI: 10.1016/j.media.2020.101905] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 11/10/2020] [Accepted: 11/11/2020] [Indexed: 12/20/2022]
Abstract
The eye affords a unique opportunity to inspect a rich part of the human microvasculature non-invasively via retinal imaging. Retinal blood vessel segmentation and classification are prime steps for the diagnosis and risk assessment of microvascular and systemic diseases. A high volume of techniques based on deep learning have been published in recent years. In this context, we review 158 papers published between 2012 and 2020, focussing on methods based on machine and deep learning (DL) for automatic vessel segmentation and classification for fundus camera images. We divide the methods into various classes by task (segmentation or artery-vein classification), technique (supervised or unsupervised, deep and non-deep learning, hand-crafted methods) and more specific algorithms (e.g. multiscale, morphology). We discuss advantages and limitations, and include tables summarising results at-a-glance. Finally, we attempt to assess the quantitative merit of DL methods in terms of accuracy improvement compared to other methods. The results allow us to offer our views on the outlook for vessel segmentation and classification for fundus camera images.
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Affiliation(s)
| | - Stephen Hogg
- VAMPIRE project, Computing (SSEN), University of Dundee, Dundee DD1 4HN, UK
| | - Tom J MacGillivray
- VAMPIRE project, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Vijayaraghavan Prathiba
- Madras Diabetes Research Foundation and Dr. Mohan's Diabetes Specialities Centre, Gopalapuram, Chennai 600086, India
| | - Rajendra Pradeepa
- Madras Diabetes Research Foundation and Dr. Mohan's Diabetes Specialities Centre, Gopalapuram, Chennai 600086, India
| | - Viswanathan Mohan
- Madras Diabetes Research Foundation and Dr. Mohan's Diabetes Specialities Centre, Gopalapuram, Chennai 600086, India
| | - Ranjit Mohan Anjana
- Madras Diabetes Research Foundation and Dr. Mohan's Diabetes Specialities Centre, Gopalapuram, Chennai 600086, India
| | - Alexander S Doney
- Division of Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, UK
| | - Colin N A Palmer
- Division of Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, UK
| | - Emanuele Trucco
- VAMPIRE project, Computing (SSEN), University of Dundee, Dundee DD1 4HN, UK
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11
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Farahani A, Mohseni H. Medical image segmentation using customized U-Net with adaptive activation functions. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05396-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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12
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Laha S, LaLonde R, Carmack AE, Foroosh H, Olson JC, Shaikh S, Bagci U. Analysis of Video Retinal Angiography With Deep Learning and Eulerian Magnification. FRONTIERS IN COMPUTER SCIENCE 2020. [DOI: 10.3389/fcomp.2020.00024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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13
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Zhang X, Chen W, Li G, Li W. The Use of Texture Features to Extract and Analyze Useful Information from Retinal Images. Comb Chem High Throughput Screen 2020; 23:313-318. [DOI: 10.2174/1386207322666191022123445] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 07/19/2019] [Accepted: 09/30/2019] [Indexed: 11/22/2022]
Abstract
Background:
The analysis of retinal images can help to detect retinal abnormalities that
are caused by cardiovascular and retinal disorders.
Objective:
In this paper, we propose methods based on texture features for mining and analyzing
information from retinal images.
Methods:
The recognition of the retinal mask region is a prerequisite for retinal image processing.
However, there is no way to automatically recognize the retinal region. By quantifying and
analyzing texture features, a method is proposed to automatically identify the retinal region. The
boundary of the circular retinal region is detected based on the image texture contrast feature,
followed by the filling of the closed circular area, and then the detected circular retinal mask region
can be obtained.
Results:
The experimental results show that the method based on the image contrast feature can be
used to detect the retinal region automatically. The average accuracy of retinal mask region detection
of images from the Digital Retinal Images for Vessel Extraction (DRIVE) database was 99.34%.
Conclusion:
This is the first time these texture features of retinal images are analyzed, and texture
features are used to recognize the circular retinal region automatically.
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Affiliation(s)
- Xiaobo Zhang
- College of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Weiyang Chen
- College of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Gang Li
- Shandong Computer Science Center (National Supercomputer Center in Jinan), Shandong Provincial Key Laboratory of Computer Networks, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Weiwei Li
- College of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
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14
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Algorithms for Diagnosis of Diabetic Retinopathy and Diabetic Macula Edema- A Review. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1307:357-373. [PMID: 32166636 DOI: 10.1007/5584_2020_499] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Human eye is one of the important organs in human body, with iris, pupil, sclera, cornea, lens, retina and optic nerve. Many important eye diseases as well as systemic diseases manifest themselves in the retina. The most widespread causes of blindness in the industrialized world are glaucoma, Age Related Macular Degeneration (ARMD), Diabetic Retinopathy (DR) and Diabetic Macula Edema (DME). The development of a retinal image analysis system is a demanding research topic for early detection, progression analysis and diagnosis of eye diseases. Early diagnosis and treatment of retinal diseases are essential to prevent vision loss. The huge and growing number of retinal disease affected patients, cost of current hospital-based detection methods (by eye care specialists) and scarcity in the number of ophthalmologists are the barriers to achieve the recommended screening compliance in the patient who is at the risk of retinal diseases. Developing an automated system which uses pattern recognition, computer vision and machine learning to diagnose retinal diseases is a potential solution to this problem. Damage to the tiny blood vessels in the retina in the posterior part of the eye due to diabetes is named as DR. Diabetes is a disease which occurs when the pancreas does not secrete enough insulin or the body does not utilize it properly. This disease slowly affects the circulatory system including that of the retina. As diabetes intensifies, the vision of a patient may start deteriorating and leading to DR. The retinal landmarks like OD and blood vessels, white lesions and red lesions are segmented to develop automated screening system for DR. DME is an advanced symptom of DR that can lead to irreversible vision loss. DME is a general term defined as retinal thickening or exudates present within 2 disk diameter of the fovea center; it can either focal or diffuse DME in distribution. In this paper, review the algorithms used in diagnosis of DR and DME.
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15
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Khawaja A, Khan TM, Khan MAU, Nawaz SJ. A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation. SENSORS (BASEL, SWITZERLAND) 2019; 19:E4949. [PMID: 31766276 PMCID: PMC6891360 DOI: 10.3390/s19224949] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 11/02/2019] [Accepted: 11/08/2019] [Indexed: 11/16/2022]
Abstract
The assessment of transformations in the retinal vascular structure has a strong potential in indicating a wide range of underlying ocular pathologies. Correctly identifying the retinal vessel map is a crucial step in disease identification, severity progression assessment, and appropriate treatment. Marking the vessels manually by a human expert is a tedious and time-consuming task, thereby reinforcing the need for automated algorithms capable of quick segmentation of retinal features and any possible anomalies. Techniques based on unsupervised learning methods utilize vessel morphology to classify vessel pixels. This study proposes a directional multi-scale line detector technique for the segmentation of retinal vessels with the prime focus on the tiny vessels that are most difficult to segment out. Constructing a directional line-detector, and using it on images having only the features oriented along the detector's direction, significantly improves the detection accuracy of the algorithm. The finishing step involves a binarization operation, which is again directional in nature, helps in achieving further performance improvements in terms of key performance indicators. The proposed method is observed to obtain a sensitivity of 0.8043, 0.8011, and 0.7974 for the Digital Retinal Images for Vessel Extraction (DRIVE), STructured Analysis of the Retina (STARE), and Child Heart And health Study in England (CHASE_DB1) datasets, respectively. These results, along with other performance enhancements demonstrated by the conducted experimental evaluation, establish the validity and applicability of directional multi-scale line detectors as a competitive framework for retinal image segmentation.
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Affiliation(s)
- Ahsan Khawaja
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad 45550, Pakistan; (T.M.K.); (S.J.N.)
| | - Tariq M. Khan
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad 45550, Pakistan; (T.M.K.); (S.J.N.)
| | | | - Syed Junaid Nawaz
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad 45550, Pakistan; (T.M.K.); (S.J.N.)
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16
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Arsalan M, Owais M, Mahmood T, Cho SW, Park KR. Aiding the Diagnosis of Diabetic and Hypertensive Retinopathy Using Artificial Intelligence-Based Semantic Segmentation. J Clin Med 2019; 8:E1446. [PMID: 31514466 PMCID: PMC6780110 DOI: 10.3390/jcm8091446] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 09/04/2019] [Accepted: 09/07/2019] [Indexed: 12/13/2022] Open
Abstract
Automatic segmentation of retinal images is an important task in computer-assisted medical image analysis for the diagnosis of diseases such as hypertension, diabetic and hypertensive retinopathy, and arteriosclerosis. Among the diseases, diabetic retinopathy, which is the leading cause of vision detachment, can be diagnosed early through the detection of retinal vessels. The manual detection of these retinal vessels is a time-consuming process that can be automated with the help of artificial intelligence with deep learning. The detection of vessels is difficult due to intensity variation and noise from non-ideal imaging. Although there are deep learning approaches for vessel segmentation, these methods require many trainable parameters, which increase the network complexity. To address these issues, this paper presents a dual-residual-stream-based vessel segmentation network (Vess-Net), which is not as deep as conventional semantic segmentation networks, but provides good segmentation with few trainable parameters and layers. The method takes advantage of artificial intelligence for semantic segmentation to aid the diagnosis of retinopathy. To evaluate the proposed Vess-Net method, experiments were conducted with three publicly available datasets for vessel segmentation: digital retinal images for vessel extraction (DRIVE), the Child Heart Health Study in England (CHASE-DB1), and structured analysis of retina (STARE). Experimental results show that Vess-Net achieved superior performance for all datasets with sensitivity (Se), specificity (Sp), area under the curve (AUC), and accuracy (Acc) of 80.22%, 98.1%, 98.2%, and 96.55% for DRVIE; 82.06%, 98.41%, 98.0%, and 97.26% for CHASE-DB1; and 85.26%, 97.91%, 98.83%, and 96.97% for STARE dataset.
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Affiliation(s)
- Muhammad Arsalan
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea.
| | - Muhammad Owais
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea.
| | - Tahir Mahmood
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea.
| | - Se Woon Cho
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea.
| | - Kang Ryoung Park
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea.
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17
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Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy. Symmetry (Basel) 2019. [DOI: 10.3390/sym11060749] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.
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18
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Khan KB, Khaliq AA, Jalil A, Iftikhar MA, Ullah N, Aziz MW, Ullah K, Shahid M. A review of retinal blood vessels extraction techniques: challenges, taxonomy, and future trends. Pattern Anal Appl 2018. [DOI: 10.1007/s10044-018-0754-8] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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19
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Kar SS, Maity SP. Automatic Detection of Retinal Lesions for Screening of Diabetic Retinopathy. IEEE Trans Biomed Eng 2018; 65:608-618. [DOI: 10.1109/tbme.2017.2707578] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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20
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L Srinidhi C, Aparna P, Rajan J. Recent Advancements in Retinal Vessel Segmentation. J Med Syst 2017; 41:70. [DOI: 10.1007/s10916-017-0719-2] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Accepted: 03/01/2017] [Indexed: 11/28/2022]
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