Fast, accurate and robust retinal vessel segmentation system

Jiang, Zhexin; Yépez, Juan; An, Sen; Ko, Seok‐Bum

doi:10.1016/j.bbe.2017.04.001

Cited by 71 publications

(35 citation statements)

References 16 publications

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“…Vessel tracking methods [12] [13] [14] capture the blood vessel profiles by tracking or tracing the blood vessels' central lines obtained by examining the zerocrossing of the gradient function. Morphological techniques [15] [16] employ mathematical transformations such as Top-hat filtering (for enhancement) and Watershed transformation (for segmentation) to identify the vessel profiles in retinal images. Since the width of blood vessels tend to decrease outward from the Optic Disk, Multi-Scale techniques [17] [18] can analyze the geometric and intensity profiles of the blood vessels at various scales to extract the width, size, and orientations for further segmentation.…”

Section: Introductionmentioning

confidence: 99%

A supervised blood vessel segmentation technique for digital Fundus images using Zernike Moment based features

et al. 2020

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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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Section: Introductionmentioning

confidence: 99%

A supervised blood vessel segmentation technique for digital Fundus images using Zernike Moment based features

et al. 2020

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“…The proposed algorithm, developed using DRIVE dataset, and achieved a detection precision of 77% and accuracy of 95%. Jiang et al [72] presented a novel work to extract the retinal vasculature structure, by using global thresholding based on morphological operations. The proposed system was tested via DRIVE and STARE datasets, and achieved an average accuracy of 95.88% for single dataset test and 95.27% for the cross-dataset test.…”

Section: Multi-scale Techniquesmentioning

confidence: 99%

Retinal Vessels Segmentation Techniques and Algorithms: A Survey

2018

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Abstract:Retinal vessels identification and localization aim to separate the different retinal vasculature structure tissues, either wide or narrow ones, from the fundus image background and other retinal anatomical structures such as optic disc, macula, and abnormal lesions. Retinal vessels identification studies are attracting more and more attention in recent years due to non-invasive fundus imaging and the crucial information contained in vasculature structure which is helpful for the detection and diagnosis of a variety of retinal pathologies included but not limited to: Diabetic Retinopathy (DR), glaucoma, hypertension, and Age-related Macular Degeneration (AMD). With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting retinal vessels are becoming more and more crucial and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for retinal vessels segmentation techniques. Firstly, a brief introduction to retinal fundus photography and imaging modalities of retinal images is given. Then, the preprocessing operations and the state of the art methods of retinal vessels identification are introduced. Moreover, the evaluation and validation of the results of retinal vessels segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for retinal vessels identification techniques.

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“…The presented technique outperforms other standard techniques. Morphology based global thresholding is proposed in Jiang et al [6] to extract the retinal structures. This technique not only provides greater accuracy and superior robustness, but also decreases the computational burden of system and reduces the execution time.…”

Section: Literature Reviewmentioning

confidence: 99%

Mathematical Morphology based Retinal Image Blood Vessels Segmentation

2019

IJITEE

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It is necessary to verify the state of blood vessel network in the retina for diagnosing various issues associated with eyes. In this research paper, an involuntary retinal vessel segmentation using mathematical morphology is proposed. The contrast of the retinal images is enhanced by contrast limited adaptive histogram equalisation technique. Ten blood vessels of the enhanced retinal image are detected using morphological processing. The hysteresis thresholding is applied on the blood vessels detected image to remove the unwanted back ground detail. Finally the properly segmented binary image of the retinal vessel is obtained using post processing process. Results of the presented method are verified by using most widely used for benchmarking retinal image databases such as, Child Heart and Health Study in England (CHASE_DB1) and Digital Retinal Images for Vessel Extraction (DRIVE) database by computing the evaluation metrics such as sensitivity, specificity, accuracy and precision. The better evaluation metrics achieved for the DRIVE dataset are 0.7493, 0.9687, 0.9524 and 0.6590, and the worst values are 0.6621, 0.9411, 0.9137 and 0.5491. The best evaluation metrics values for the CHASE_DB1 dataset are 0.5058, 0.8947, 0.9382 and 0.8856, and the worst values are 0.5639, 0.9581, 0.9137 and 0.7110. The investigational results show that the suggested approach provides the excellent accuracy in comparison with other approaches

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Fast, accurate and robust retinal vessel segmentation system

Cited by 71 publications

References 16 publications

A supervised blood vessel segmentation technique for digital Fundus images using Zernike Moment based features

A supervised blood vessel segmentation technique for digital Fundus images using Zernike Moment based features

Retinal Vessels Segmentation Techniques and Algorithms: A Survey

Mathematical Morphology based Retinal Image Blood Vessels Segmentation

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