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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: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Erwin, Safmi A, Desiani A, Suprihatin B, Fathoni. The Augmentation Data of Retina Image for Blood Vessel Segmentation Using U-Net Convolutional Neural Network Method. Int J Comp Intel Appl 2022. [DOI: 10.1142/s1469026822500043] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The retina is the most important part of the eye. By proper feature extraction, it can be the first step to detect a disease. Morphology of retina blood vessels can be used to identify and classify a disease. A step, such as segmentation and analysis of retinal blood vessels, can assist medical personnel in detecting the severity of a disease. In this paper, vascular segmentation using U-net architecture in the Convolutional Neural Network (CNN) method is proposed to train a sematic segmentation model in retinal blood vessel. In addition, the Contrast Limited Adaptive Histogram Equalization (CLAHE) method is used to increase the contrast of the grayscale and Median Filter is used to obtain better image quality. Data augmentation is also used to maximize the number of datasets owned to make more. The proposed method allows for easier implementation. In this study, the dataset used was STARE with the result of accuracy, sensitivity, specificity, precision, and F1-score that reached 97.64%, 78.18%, 99.20%, 88.77%, and 82.91%.
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Affiliation(s)
- Erwin
- Department of Computer Engineering, University of Sriwijaya, Jalan Raya Palembang-Unsri KM 32, Indralaya, Indonesia
| | - Asri Safmi
- Department of Computer Engineering, University of Sriwijaya, Jalan Raya Palembang-Unsri KM 32, Indralaya, Indonesia
| | - Anita Desiani
- Department of Mathematics, University of Sriwijaya, Jalan Raya Palembang-Unsri KM 32, Indralaya, South of Sumatera, Indonesia
| | - Bambang Suprihatin
- Department of Mathematics, University of Sriwijaya, Jalan Raya Palembang-Unsri KM 32, Indralaya, South of Sumatera, Indonesia
| | - Fathoni
- Department of Information System, University of Sriwijaya, Jalan Raya Palembang-Unsri KM 32, Indralaya, South of Sumatera, Indonesia
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Rehman A, Harouni M, Karimi M, Saba T, Bahaj SA, Awan MJ. Microscopic retinal blood vessels detection and segmentation using support vector machine and K-nearest neighbors. Microsc Res Tech 2022; 85:1899-1914. [PMID: 35037735 DOI: 10.1002/jemt.24051] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 11/14/2021] [Accepted: 12/12/2021] [Indexed: 01/08/2023]
Abstract
The retina is the deepest layer of texture covering the rear of the eye, recorded by fundus images. Vessel detection and segmentation are useful in disease diagnosis. The retina's blood vessels could help diagnose maladies such as glaucoma, diabetic retinopathy, and blood pressure. A mix of supervised and unsupervised strategies exists for the detection and segmentation of blood vessels images. The tree structure of retinal blood vessels, their random area, and different thickness have caused vessel detection difficulties at machine learning calculations. Since the green band of retinal images conveys more information about the vessels, they are utilized for microscopic vessels detection. The current research proposes an administered calculation for segmentation of retinal vessels, where two upgrading stages depending on filtering and comparative histogram were applied after pre-processing and image quality improvement. At that point, statistical features of vessel tracking, maximum curvature and curvelet coefficient are extracted for each pixel. The extracted features are classified by support vector machine and the k-nearest neighbors. The morphological operators then enhance the classified image at the final stage to segment with higher accuracy. The dice coefficient is utilized for the evaluation of the proposed method. The proposed approach is concluded to be better than different strategies with a normal of 92%.
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Affiliation(s)
- Amjad Rehman
- Artificial Intelligence & Data Analytics Lab CCIS, Prince Sultan University, Riyadh, Saudi Arabia
| | - Majid Harouni
- Department of Computer Engineering, Dolatabad Branch, Islamic Azad University, Isfahan, Iran
| | - Mohsen Karimi
- Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan, University of Medical Sciences, Isfahan, Iran
| | - Tanzila Saba
- Artificial Intelligence & Data Analytics Lab CCIS, Prince Sultan University, Riyadh, Saudi Arabia
| | - Saeed Ali Bahaj
- MIS Department College of Business Administration, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
| | - Mazar Javed Awan
- Department of Software Engineering, University of Management and Technology, Lahore, Pakistan
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Badawi SA, Fraz MM, Shehzad M, Mahmood I, Javed S, Mosalam E, Nileshwar AK. Detection and Grading of Hypertensive Retinopathy Using Vessels Tortuosity and Arteriovenous Ratio. J Digit Imaging 2022; 35:281-301. [PMID: 35013827 PMCID: PMC8921404 DOI: 10.1007/s10278-021-00545-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 11/09/2021] [Accepted: 11/11/2021] [Indexed: 10/19/2022] Open
Abstract
Hypertensive retinopathy (HR) refers to changes in the morphological diameter of the retinal vessels due to persistent high blood pressure. Early detection of such changes helps in preventing blindness or even death due to stroke. These changes can be quantified by computing the arteriovenous ratio and the tortuosity severity in the retinal vasculature. This paper presents a decision support system for detecting and grading HR using morphometric analysis of retinal vasculature, particularly measuring the arteriovenous ratio (AVR) and retinal vessel tortuosity. In the first step, the retinal blood vessels are segmented and classified as arteries and veins. Then, the width of arteries and veins is measured within the region of interest around the optic disk. Next, a new iterative method is proposed to compute the AVR from the caliber measurements of arteries and veins using Parr-Hubbard and Knudtson methods. Moreover, the retinal vessel tortuosity severity index is computed for each image using 14 tortuosity severity metrics. In the end, a hybrid decision support system is proposed for the detection and grading of HR using AVR and tortuosity severity index. Furthermore, we present a new publicly available retinal vessel morphometry (RVM) dataset to evaluate the proposed methodology. The RVM dataset contains 504 retinal images with pixel-level annotations for vessel segmentation, artery/vein classification, and optic disk localization. The image-level labels for vessel tortuosity index and HR grade are also available. The proposed methods of iterative AVR measurement, tortuosity index, and HR grading are evaluated using the new RVM dataset. The results indicate that the proposed method gives superior performance than existing methods. The presented methodology is a novel advancement in automated detection and grading of HR, which can potentially be used as a clinical decision support system.
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Affiliation(s)
- Sufian A Badawi
- School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Muhammad Moazam Fraz
- School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
| | - Muhammad Shehzad
- School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Imran Mahmood
- School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Sajid Javed
- Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, UAE
| | - Emad Mosalam
- Department of Ophthalmology, RAK Medical and Health Sciences University, Ras Al Khaimah, UAE.,Department of Ophthalmology, Saqr Hospital, Ministry of Health and Prevention, Ras Al Khaimah, UAE
| | - Ajay Kamath Nileshwar
- Department of Ophthalmology, RAK Medical and Health Sciences University, Ras Al Khaimah, UAE.,Department of Ophthalmology, Saqr Hospital, Ministry of Health and Prevention, Ras Al Khaimah, UAE
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Wahid FF, Sugandhi K, Raju G. A Fusion Based Approach for Blood Vessel Segmentation from Fundus Images by Separating Brighter Optic Disc. Pattern Recognit Image Anal 2021. [DOI: 10.1134/s105466182104026x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Kovács G, Fazekas A. A new baseline for retinal vessel segmentation: Numerical identification and correction of methodological inconsistencies affecting 100+ papers. Med Image Anal 2021; 75:102300. [PMID: 34814057 DOI: 10.1016/j.media.2021.102300] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 09/20/2021] [Accepted: 11/04/2021] [Indexed: 12/18/2022]
Abstract
In the last 15 years, the segmentation of vessels in retinal images has become an intensively researched problem in medical imaging, with hundreds of algorithms published. One of the de facto benchmarking data sets of vessel segmentation techniques is the DRIVE data set. Since DRIVE contains a predefined split of training and test images, the published performance results of the various segmentation techniques should provide a reliable ranking of the algorithms. Including more than 100 papers in the study, we performed a detailed numerical analysis of the coherence of the published performance scores. We found inconsistencies in the reported scores related to the use of the field of view (FoV), which has a significant impact on the performance scores. We attempted to eliminate the biases using numerical techniques to provide a more realistic picture of the state of the art. Based on the results, we have formulated several findings, most notably: despite the well-defined test set of DRIVE, most rankings in published papers are based on non-comparable figures; in contrast to the near-perfect accuracy scores reported in the literature, the highest accuracy score achieved to date is 0.9582 in the FoV region, which is 1% higher than that of human annotators. The methods we have developed for identifying and eliminating the evaluation biases can be easily applied to other domains where similar problems may arise.
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Affiliation(s)
- György Kovács
- Analytical Minds Ltd., Árpád street 5, Beregsurány 4933, Hungary.
| | - Attila Fazekas
- University of Debrecen, Faculty of Informatics, P.O.BOX 400, Debrecen 4002, Hungary.
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8
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Adapa D, Joseph Raj AN, Alisetti SN, Zhuang Z, K. G, Naik G. A supervised blood vessel segmentation technique for digital Fundus images using Zernike Moment based features. PLoS One 2020; 15:e0229831. [PMID: 32142540 PMCID: PMC7059933 DOI: 10.1371/journal.pone.0229831] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 02/16/2020] [Indexed: 11/18/2022] Open
Abstract
This paper proposes a new supervised method for blood vessel segmentation using Zernike moment-based shape descriptors. The method implements a pixel wise classification by computing a 11-D feature vector comprising of both statistical (gray-level) features and shape-based (Zernike moment) features. Also the feature set contains optimal coefficients of the Zernike Moments which were derived based on the maximum differentiability between the blood vessel and background pixels. A manually selected training points obtained from the training set of the DRIVE dataset, covering all possible manifestations were used for training the ANN-based binary classifier. The method was evaluated on unknown test samples of DRIVE and STARE databases and returned accuracies of 0.945 and 0.9486 respectively, outperforming other existing supervised learning methods. Further, the segmented outputs were able to cover thinner blood vessels better than previous methods, aiding in early detection of pathologies.
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Affiliation(s)
- Dharmateja Adapa
- Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Department of Electronic Engineering, College of Engineering, Shantou University, Shantou, Guangdong, China
| | - Alex Noel Joseph Raj
- Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Department of Electronic Engineering, College of Engineering, Shantou University, Shantou, Guangdong, China
| | - Sai Nikhil Alisetti
- Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Department of Electronic Engineering, College of Engineering, Shantou University, Shantou, Guangdong, China
| | - Zhemin Zhuang
- Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Department of Electronic Engineering, College of Engineering, Shantou University, Shantou, Guangdong, China
| | - Ganesan K.
- TIFAC-CORE, School of Electronics, Vellore Institute of Technology, Vellore, India
| | - Ganesh Naik
- MARCS Institute, Western Sydney University, Australia
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