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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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Zhang H, Ni W, Luo Y, Feng Y, Song R, Wang X. TUnet-LBF: Retinal fundus image fine segmentation model based on transformer Unet network and LBF. Comput Biol Med 2023; 159:106937. [PMID: 37084640 DOI: 10.1016/j.compbiomed.2023.106937] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 04/01/2023] [Accepted: 04/13/2023] [Indexed: 04/23/2023]
Abstract
Segmentation of retinal fundus images is a crucial part of medical diagnosis. Automatic extraction of blood vessels in low-quality retinal images remains a challenging problem. In this paper, we propose a novel two-stage model combining Transformer Unet (TUnet) and local binary energy function model (LBF), TUnet-LBF, for coarse to fine segmentation of retinal vessels. In the coarse segmentation stage, the global topological information of blood vessels is obtained by TUnet. The neural network outputs the initial contour and the probability maps, which are input to the fine segmentation stage as the priori information. In the fine segmentation stage, an energy modulated LBF model is proposed to obtain the local detail information of blood vessels. The proposed model reaches accuracy (Acc) of 0.9650, 0.9681 and 0.9708 on the public datasets DRIVE, STARE and CHASE_DB1 respectively. The experimental results demonstrate the effectiveness of each component in the proposed model.
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Affiliation(s)
- Hanyu Zhang
- School of Geography, Liaoning Normal University, Dalian City, 116029, China; School of Computer and Information Technology, Liaoning Normal University, Dalian City, 116029, China; College of Information Science and Engineering, Northeastern University, Shenyang, 110167, China.
| | - Weihan Ni
- School of Computer and Information Technology, Liaoning Normal University, Dalian City, 116029, China.
| | - Yi Luo
- College of Information Science and Engineering, Northeastern University, Shenyang, 110167, China.
| | - Yining Feng
- School of Geography, Liaoning Normal University, Dalian City, 116029, China.
| | - Ruoxi Song
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Xianghai Wang
- School of Geography, Liaoning Normal University, Dalian City, 116029, China; School of Computer and Information Technology, Liaoning Normal University, Dalian City, 116029, China.
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3
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Lehilahy M, Ferdi Y. Identification of exon locations in DNA sequences using a fractional digital anti-notch filter. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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4
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Nage P, Shitole S, Kokare M. An intelligent approach for detection and grading of diabetic retinopathy and diabetic macular edema using retinal images. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2023. [DOI: 10.1080/21681163.2022.2164358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Pranoti Nage
- Computer Science & Technology, Usha Mittal Institute of Technology for Women, S.N.D.T. Women’s University, Mumbai, India
| | - Sanjay Shitole
- Information Technology, Usha Mittal Institute of Technology for Women, S.N.D.T. Women’s University, Mumbai, India
| | - Manesh Kokare
- Centre of Excellence in Signal & Image Processing, Shri Guru Gobind Singhji Institute of Technology, Nanded, India
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5
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Tan Y, Yang KF, Zhao SX, Li YJ. Retinal Vessel Segmentation With Skeletal Prior and Contrastive Loss. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2238-2251. [PMID: 35320091 DOI: 10.1109/tmi.2022.3161681] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The morphology of retinal vessels is closely associated with many kinds of ophthalmic diseases. Although huge progress in retinal vessel segmentation has been achieved with the advancement of deep learning, some challenging issues remain. For example, vessels can be disturbed or covered by other components presented in the retina (such as optic disc or lesions). Moreover, some thin vessels are also easily missed by current methods. In addition, existing fundus image datasets are generally tiny, due to the difficulty of vessel labeling. In this work, a new network called SkelCon is proposed to deal with these problems by introducing skeletal prior and contrastive loss. A skeleton fitting module is developed to preserve the morphology of the vessels and improve the completeness and continuity of thin vessels. A contrastive loss is employed to enhance the discrimination between vessels and background. In addition, a new data augmentation method is proposed to enrich the training samples and improve the robustness of the proposed model. Extensive validations were performed on several popular datasets (DRIVE, STARE, CHASE, and HRF), recently developed datasets (UoA-DR, IOSTAR, and RC-SLO), and some challenging clinical images (from RFMiD and JSIEC39 datasets). In addition, some specially designed metrics for vessel segmentation, including connectivity, overlapping area, consistency of vessel length, revised sensitivity, specificity, and accuracy were used for quantitative evaluation. The experimental results show that, the proposed model achieves state-of-the-art performance and significantly outperforms compared methods when extracting thin vessels in the regions of lesions or optic disc. Source code is available at https://www.github.com/tyb311/SkelCon.
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Sindhusaranya B, Geetha M, Rajesh T, Kavitha M. Hybrid algorithm for retinal blood vessel segmentation using different pattern recognition techniques. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-221137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Blood vessel segmentation of the retina has become a necessary step in automatic disease identification and planning treatment in the field of Ophthalmology. To identify the disease properly, both thick and thin blood vessels should be distinguished clearly. Diagnosis of disease would be simple and easier only when the blood vessels are segmented accurately. Existing blood vessel segmentation methods are not supporting well to overcome the poor accuracy and low generalization problems because of the complex blood vessel structure of the retina. In this study, a hybrid algorithm is proposed using binarization, exclusively for segmenting the vessels from a retina image to enhance the exactness and specificity of segmentation of an image. The proposed algorithm extracts the advantages of pattern recognition techniques, such as Matched Filter (MF), Matched Filter with First-order Derivation of Gaussian (MF-FDOG), Multi-Scale Line Detector (MSLD) algorithms and developed as a hybrid algorithm. This algorithm is authenticated with the openly accessible dataset DRIVE. Using Python with OpenCV, the algorithm simulation results had attained an accurateness of 0.9602, a sensitivity of 0.6246, and a specificity of 0.9815 for the dataset. Simulation outcomes proved that the proposed hybrid algorithm accurately segments the blood vessels of the retina compared to the existing methodologies.
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Affiliation(s)
- B. Sindhusaranya
- Department of Electronics and Communication Engineering, Ponjesly College of Engineering, Nagercoil, Tamil Nadu, India
| | - M.R. Geetha
- Department of Electronics and Communication Engineering, Ponjesly College of Engineering, Nagercoil, Tamil Nadu, India
| | - T. Rajesh
- Department of Electronics and Communication Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, India
| | - M.R. Kavitha
- Department of Electronics and Communication Engineering, Ponjesly College of Engineering, Nagercoil, Tamil Nadu, India
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Mahapatra S, Agrawal S, Mishro PK, Pachori RB. A novel framework for retinal vessel segmentation using optimal improved frangi filter and adaptive weighted spatial FCM. Comput Biol Med 2022; 147:105770. [DOI: 10.1016/j.compbiomed.2022.105770] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 06/08/2022] [Accepted: 06/19/2022] [Indexed: 11/28/2022]
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Wan J, Yue S, Ma J, Ma X. A coarse-to-fine full attention guided capsule network for medical image segmentation. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103682] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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9
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Arora S, Mathur T, Agarwal S, Tiwari K, Gupta P. Applications of fractional calculus in computer vision: A survey. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.10.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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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] [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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Muresan CI, Birs IR, Dulf EH, Copot D, Miclea L. A Review of Recent Advances in Fractional-Order Sensing and Filtering Techniques. SENSORS 2021; 21:s21175920. [PMID: 34502811 PMCID: PMC8434365 DOI: 10.3390/s21175920] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 08/30/2021] [Accepted: 08/30/2021] [Indexed: 11/16/2022]
Abstract
The present manuscript aims at raising awareness of the endless possibilities of fractional calculus applied not only to system identification and control engineering, but also into sensing and filtering domains. The creation of the fractance device has enabled the physical realization of a new array of sensors capable of gathering more information. The same fractional-order electronic component has led to the possibility of exploring analog filtering techniques from a practical perspective, enlarging the horizon to a wider frequency range, with increased robustness to component variation, stability and noise reduction. Furthermore, fractional-order digital filters have developed to provide an alternative solution to higher-order integer-order filters, with increased design flexibility and better performance. The present study is a comprehensive review of the latest advances in fractional-order sensors and filters, with a focus on design methodologies and their real-life applicability reported in the last decade. The potential enhancements brought by the use of fractional calculus have been exploited as well in sensing and filtering techniques. Several extensions of the classical sensing and filtering methods have been proposed to date. The basics of fractional-order filters are reviewed, with a focus on the popular fractional-order Kalman filter, as well as those related to sensing. A detailed presentation of fractional-order filters is included in applications such as data transmission and networking, electrical and chemical engineering, biomedicine and various industrial fields.
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Affiliation(s)
- Cristina I. Muresan
- Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania; (C.I.M.); (E.H.D.); (L.M.)
| | - Isabela R. Birs
- Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania; (C.I.M.); (E.H.D.); (L.M.)
- Dynamical Systems and Control Research Group, Ghent University, 9052 Ghent, Belgium;
- Core Lab EEDT, Flanders Make Consortium, 9052 Ghent, Belgium
- Correspondence:
| | - Eva H. Dulf
- Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania; (C.I.M.); (E.H.D.); (L.M.)
| | - Dana Copot
- Dynamical Systems and Control Research Group, Ghent University, 9052 Ghent, Belgium;
- Core Lab EEDT, Flanders Make Consortium, 9052 Ghent, Belgium
| | - Liviu Miclea
- Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania; (C.I.M.); (E.H.D.); (L.M.)
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12
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Toptaş B, Hanbay D. Retinal blood vessel segmentation using pixel-based feature vector. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103053] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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13
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Chavez T, Vohra N, Bailey K, El-Shenawee M, Wu J. Supervised Bayesian learning for breast cancer detection in terahertz imaging. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102949] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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14
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Mardani K, Maghooli K. Enhancing retinal blood vessel segmentation in medical images using combined segmentation modes extracted by DBSCAN and morphological reconstruction. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102837] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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15
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Ramos-Soto O, Rodríguez-Esparza E, Balderas-Mata SE, Oliva D, Hassanien AE, Meleppat RK, Zawadzki RJ. An efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic filtering. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 201:105949. [PMID: 33567382 DOI: 10.1016/j.cmpb.2021.105949] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 01/18/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Automatic segmentation of retinal blood vessels makes a major contribution in CADx of various ophthalmic and cardiovascular diseases. A procedure to segment thin and thick retinal vessels is essential for medical analysis and diagnosis of related diseases. In this article, a novel methodology for robust vessel segmentation is proposed, handling the existing challenges presented in the literature. METHODS The proposed methodology consists of three stages, pre-processing, main processing, and post-processing. The first stage consists of applying filters for image smoothing. The main processing stage is divided into two configurations, the first to segment thick vessels through the new optimized top-hat, homomorphic filtering, and median filter. Then, the second configuration is used to segment thin vessels using the proposed optimized top-hat, homomorphic filtering, matched filter, and segmentation using the MCET-HHO multilevel algorithm. Finally, morphological image operations are carried out in the post-processing stage. RESULTS The proposed approach was assessed by using two publicly available databases (DRIVE and STARE) through three performance metrics: specificity, sensitivity, and accuracy. Analyzing the obtained results, an average of 0.9860, 0.7578 and 0.9667 were respectively achieved for DRIVE dataset and 0.9836, 0.7474 and 0.9580 for STARE dataset. CONCLUSIONS The numerical results obtained by the proposed technique, achieve competitive average values with the up-to-date techniques. The proposed approach outperform all leading unsupervised methods discussed in terms of specificity and accuracy. In addition, it outperforms most of the state-of-the-art supervised methods without the computational cost associated with these algorithms. Detailed visual analysis has shown that a more precise segmentation of thin vessels was possible with the proposed approach when compared with other procedures.
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Affiliation(s)
- Oscar Ramos-Soto
- División de Electrónica y Computación, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, C.P. 44430, Guadalajara, Jal., Mexico.
| | - Erick Rodríguez-Esparza
- División de Electrónica y Computación, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, C.P. 44430, Guadalajara, Jal., Mexico; DeustoTech, Faculty of Engineering, University of Deusto, Av. Universidades, 24, 48007 Bilbao, Spain.
| | - Sandra E Balderas-Mata
- División de Electrónica y Computación, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, C.P. 44430, Guadalajara, Jal., Mexico.
| | - Diego Oliva
- División de Electrónica y Computación, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, C.P. 44430, Guadalajara, Jal., Mexico; IN3 - Computer Science Dept., Universitat Oberta de Catalunya, Castelldefels, Spain.
| | | | - Ratheesh K Meleppat
- UC Davis Eyepod Imaging Laboratory, Dept. of Cell Biology and Human Anatomy, University of California Davis, Davis, CA 95616, USA; Dept. of Ophthalmology & Vision Science, University of California Davis, Sacramento, CA, USA.
| | - Robert J Zawadzki
- UC Davis Eyepod Imaging Laboratory, Dept. of Cell Biology and Human Anatomy, University of California Davis, Davis, CA 95616, USA; Dept. of Ophthalmology & Vision Science, University of California Davis, Sacramento, CA, USA.
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