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Wang Y, Li H. A Novel Single-Sample Retinal Vessel Segmentation Method Based on Grey Relational Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:4326. [PMID: 39001106 PMCID: PMC11244310 DOI: 10.3390/s24134326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 06/23/2024] [Accepted: 07/02/2024] [Indexed: 07/16/2024]
Abstract
Accurate segmentation of retinal vessels is of great significance for computer-aided diagnosis and treatment of many diseases. Due to the limited number of retinal vessel samples and the scarcity of labeled samples, and since grey theory excels in handling problems of "few data, poor information", this paper proposes a novel grey relational-based method for retinal vessel segmentation. Firstly, a noise-adaptive discrimination filtering algorithm based on grey relational analysis (NADF-GRA) is designed to enhance the image. Secondly, a threshold segmentation model based on grey relational analysis (TS-GRA) is designed to segment the enhanced vessel image. Finally, a post-processing stage involving hole filling and removal of isolated pixels is applied to obtain the final segmentation output. The performance of the proposed method is evaluated using multiple different measurement metrics on publicly available digital retinal DRIVE, STARE and HRF datasets. Experimental analysis showed that the average accuracy and specificity on the DRIVE dataset were 96.03% and 98.51%. The mean accuracy and specificity on the STARE dataset were 95.46% and 97.85%. Precision, F1-score, and Jaccard index on the HRF dataset all demonstrated high-performance levels. The method proposed in this paper is superior to the current mainstream methods.
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Affiliation(s)
- Yating Wang
- School of Information Science and Technology, Nantong University, Nantong 226019, China
| | - Hongjun Li
- School of Information Science and Technology, Nantong University, Nantong 226019, China
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2
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Ma Z, Li X. An improved supervised and attention mechanism-based U-Net algorithm for retinal vessel segmentation. Comput Biol Med 2024; 168:107770. [PMID: 38056215 DOI: 10.1016/j.compbiomed.2023.107770] [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: 08/10/2023] [Revised: 11/08/2023] [Accepted: 11/26/2023] [Indexed: 12/08/2023]
Abstract
The segmentation results of retinal blood vessels are crucial for automatically diagnosing ophthalmic diseases such as diabetic retinopathy, hypertension, cardiovascular and cerebrovascular diseases. To improve the accuracy of vessel segmentation and better extract information about small vessels and edges, we introduce the U-Net algorithm with a supervised attention mechanism for retinal vessel segmentation. We achieve this by introducing a decoder fusion module (DFM) in the encoding part, effectively combining different convolutional blocks to extract features comprehensively. Additionally, in the decoding part of U-Net, we propose the context squeeze and excitation (CSE) decoding module to enhance important contextual feature information and the detection of tiny blood vessels. For the final output, we introduce the supervised fusion mechanism (SFM), which combines multiple branches from shallow to deep layers, effectively fusing multi-scale features and capturing information from different levels, fully integrating low-level and high-level features to improve segmentation performance. Our experimental results on the public datasets of DRIVE, STARE, and CHASED_B1 demonstrate the excellent performance of our proposed network.
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Affiliation(s)
- Zhendi Ma
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
| | - Xiaobo Li
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China.
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3
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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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Huang KW, Yang YR, Huang ZH, Liu YY, Lee SH. Retinal Vascular Image Segmentation Using Improved UNet Based on Residual Module. Bioengineering (Basel) 2023; 10:722. [PMID: 37370653 DOI: 10.3390/bioengineering10060722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 06/01/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
In recent years, deep learning technology for clinical diagnosis has progressed considerably, and the value of medical imaging continues to increase. In the past, clinicians evaluated medical images according to their individual expertise. In contrast, the application of artificial intelligence technology for automatic analysis and diagnostic assistance to support clinicians in evaluating medical information more efficiently has become an important trend. In this study, we propose a machine learning architecture designed to segment images of retinal blood vessels based on an improved U-Net neural network model. The proposed model incorporates a residual module to extract features more effectively, and includes a full-scale skip connection to combine low level details with high-level features at different scales. The results of an experimental evaluation show that the model was able to segment images of retinal vessels accurately. The proposed method also outperformed several existing models on the benchmark datasets DRIVE and ROSE, including U-Net, ResUNet, U-Net3+, ResUNet++, and CaraNet.
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Affiliation(s)
- Ko-Wei Huang
- Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
| | - Yao-Ren Yang
- Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
| | - Zih-Hao Huang
- Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
| | - Yi-Yang Liu
- Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
- Department of Urology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan
| | - Shih-Hsiung Lee
- Department of Intelligent Commerce, National Kaohsiung University of Science and Technology, Kaohsiung 82444, Taiwan
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Iqbal S, Khan TM, Naveed K, Naqvi SS, Nawaz SJ. Recent trends and advances in fundus image analysis: A review. Comput Biol Med 2022; 151:106277. [PMID: 36370579 DOI: 10.1016/j.compbiomed.2022.106277] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/19/2022] [Accepted: 10/30/2022] [Indexed: 11/05/2022]
Abstract
Automated retinal image analysis holds prime significance in the accurate diagnosis of various critical eye diseases that include diabetic retinopathy (DR), age-related macular degeneration (AMD), atherosclerosis, and glaucoma. Manual diagnosis of retinal diseases by ophthalmologists takes time, effort, and financial resources, and is prone to error, in comparison to computer-aided diagnosis systems. In this context, robust classification and segmentation of retinal images are primary operations that aid clinicians in the early screening of patients to ensure the prevention and/or treatment of these diseases. This paper conducts an extensive review of the state-of-the-art methods for the detection and segmentation of retinal image features. Existing notable techniques for the detection of retinal features are categorized into essential groups and compared in depth. Additionally, a summary of quantifiable performance measures for various important stages of retinal image analysis, such as image acquisition and preprocessing, is provided. Finally, the widely used in the literature datasets for analyzing retinal images are described and their significance is emphasized.
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Affiliation(s)
- Shahzaib Iqbal
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan
| | - Tariq M Khan
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.
| | - Khuram Naveed
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan; Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark
| | - Syed S Naqvi
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan
| | - Syed Junaid Nawaz
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan
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Ni J, Liu J, Li X, Chen Z. SFA-Net: Scale and Feature Aggregate Network for Retinal Vessel Segmentation. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4695136. [PMID: 36312595 PMCID: PMC9616669 DOI: 10.1155/2022/4695136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/22/2022] [Accepted: 10/02/2022] [Indexed: 11/18/2022]
Abstract
A U-Net-based network has achieved competitive performance in retinal vessel segmentation. Previous work has focused on using multilevel high-level features to improve segmentation accuracy but has ignored the importance of shallow-level features. In addition, multiple upsampling and convolution operations may destroy the semantic feature information contained in the decoder layer. To address these problems, we propose a scale and feature aggregate network (SFA-Net), which can make full use of multiscale high-level feature information and shallow features. In this paper, a residual atrous spatial feature aggregate block (RASF) is embedded at the end of the encoder to learn multiscale information. Furthermore, an attentional feature module (AFF) is proposed to enhance the effective fusion between shallow and high-level features. In addition, we designed the multi-path feature fusion (MPF) block to fuse high-level features of different decoder layers, which aims to learn the relationship between the high-level features of different paths and alleviate the information loss. We apply the network to the three benchmark datasets (DRIVE, STARE, and CHASE_DB1) and compare them with the other current state-of-the-art methods. The experimental results demonstrated that the proposed SFA-Net performs effectively, indicating that the network is suitable for processing some complex medical images.
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Affiliation(s)
- Jiajia Ni
- College of Internet of Things Engineering, Hohai University, Changzhou, China
| | - Jinhui Liu
- College of Internet of Things Engineering, Hohai University, Changzhou, China
| | - Xuefei Li
- College of Internet of Things Engineering, Hohai University, Changzhou, China
| | - Zhengming Chen
- College of Internet of Things Engineering, Hohai University, Changzhou, China
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Rodrigues EO, Rodrigues LO, Machado JHP, Casanova D, Teixeira M, Oliva JT, Bernardes G, Liatsis P. Local-Sensitive Connectivity Filter (LS-CF): A Post-Processing Unsupervised Improvement of the Frangi, Hessian and Vesselness Filters for Multimodal Vessel Segmentation. J Imaging 2022; 8:jimaging8100291. [PMID: 36286385 PMCID: PMC9604711 DOI: 10.3390/jimaging8100291] [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: 08/05/2022] [Revised: 09/13/2022] [Accepted: 09/16/2022] [Indexed: 11/07/2022] Open
Abstract
A retinal vessel analysis is a procedure that can be used as an assessment of risks to the eye. This work proposes an unsupervised multimodal approach that improves the response of the Frangi filter, enabling automatic vessel segmentation. We propose a filter that computes pixel-level vessel continuity while introducing a local tolerance heuristic to fill in vessel discontinuities produced by the Frangi response. This proposal, called the local-sensitive connectivity filter (LS-CF), is compared against a naive connectivity filter to the baseline thresholded Frangi filter response and to the naive connectivity filter response in combination with the morphological closing and to the current approaches in the literature. The proposal was able to achieve competitive results in a variety of multimodal datasets. It was robust enough to outperform all the state-of-the-art approaches in the literature for the OSIRIX angiographic dataset in terms of accuracy and 4 out of 5 works in the case of the IOSTAR dataset while also outperforming several works in the case of the DRIVE and STARE datasets and 6 out of 10 in the CHASE-DB dataset. For the CHASE-DB, it also outperformed all the state-of-the-art unsupervised methods.
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Affiliation(s)
- Erick O. Rodrigues
- Department of Academic Informatics (DAINF), Universidade Tecnologica Federal do Parana (UTFPR), Pato Branco 85503-390, PR, Brazil
- Correspondence:
| | - Lucas O. Rodrigues
- Graduate Program of Sciences Applied to Health Products, Universidade Federal Fluminense (UFF), Niteroi 24241-000, RJ, Brazil
| | - João H. P. Machado
- Department of Academic Informatics (DAINF), Universidade Tecnologica Federal do Parana (UTFPR), Pato Branco 85503-390, PR, Brazil
| | - Dalcimar Casanova
- Department of Academic Informatics (DAINF), Universidade Tecnologica Federal do Parana (UTFPR), Pato Branco 85503-390, PR, Brazil
| | - Marcelo Teixeira
- Department of Academic Informatics (DAINF), Universidade Tecnologica Federal do Parana (UTFPR), Pato Branco 85503-390, PR, Brazil
| | - Jeferson T. Oliva
- Department of Academic Informatics (DAINF), Universidade Tecnologica Federal do Parana (UTFPR), Pato Branco 85503-390, PR, Brazil
| | - Giovani Bernardes
- Institute of Technological Sciences (ICT), Universidade Federal de Itajuba (UNIFEI), Itabira 35903-087, MG, Brazil
| | - Panos Liatsis
- Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
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