A new robust method for blood vessel segmentation in retinal fundus images based on weighted line detector and hidden Markov model

Zhou, Chao; Zhang, Xiaogang; Chen, Hua

doi:10.1016/j.cmpb.2019.105231

Cited by 52 publications

(16 citation statements)

References 45 publications

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“…And there are 16 methods compared with the proposed method. The AUC of the proposed method is highest except Zhou et al [ 18 ] which is 0.0004 and Wu et al [ 19 ] which is 0.008 better than the proposed method. On the STARE dataset, the proposed method has AUC of 0.9893, accuracy of 0.9683, sensitivity of 0.7747, specificity of 0.9910, F 1 score of 0.8369, and MCC of 0.8227.…”

Section: Discussionmentioning

confidence: 93%

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Retinal Vessel Automatic Segmentation Using SegNet

Wang

Liang

et al. 2022

Computational and Mathematical Methods in Medicine

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Extracting retinal vessels accurately is very important for diagnosing some diseases such as diabetes retinopathy, hypertension, and cardiovascular. Clinically, experienced ophthalmologists diagnose these diseases through segmenting retinal vessels manually and analysing its structural feature, such as tortuosity and diameter. However, manual segmentation of retinal vessels is a time-consuming and laborious task with strong subjectivity. The automatic segmentation technology of retinal vessels can not only reduce the burden of ophthalmologists but also effectively solve the problem that is a lack of experienced ophthalmologists in remote areas. Therefore, the automatic segmentation technology of retinal vessels is of great significance for clinical auxiliary diagnosis and treatment of ophthalmic diseases. A method using SegNet is proposed in this paper to improve the accuracy of the retinal vessel segmentation. The performance of the retinal vessel segmentation model with SegNet is evaluated on the three public datasets (DRIVE, STARE, and HRF) and achieved accuracy of 0.9518, 0.9683, and 0.9653, sensitivity of 0.7580, 0.7747, and 0.7070, specificity of 0.9804, 0.9910, and 0.9885, F 1 score of 0.7992, 0.8369, and 0.7918, MCC of 0.7749, 0.8227, and 0.7643, and AUC of 0.9750, 0.9893, and 0.9740, respectively. The experimental results showed that the method proposed in this research presented better results than many classical methods studied and may be expected to have clinical application prospects.

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Section: Discussionmentioning

confidence: 93%

“…Zhou et al proposed a method with a line detector, hidden Markov model (HMM), and a denoising approach to resolve this problem. It tested on the DRIVE and STARE datasets and obtained high specificity of 0.9803 and 0.9992 [ 18 ]. Most of the above researches showed that thin or low-contrast vessels have low segmentation sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Retinal Vessel Automatic Segmentation Using SegNet

Wang

Liang

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Therefore, the processing and analysis of the retinal fundus image is very important. The application of machine vision in the retinal fundus image has attracted the attention of many researchers, and a certain effect is achieved [17,18,19]. Intelligent algorithm, as a kind of optimization algorithm, is also applied to the processing of the retinal fundus image, such as the use of fireflies algorithm to locate the disc [20].…”

Section: A Overview Of Fundus Imagesmentioning

confidence: 99%

An Improved SFLA-Kmeans Algorithm Based on Approximate Backbone and Its Application in Retinal Fundus Image

et al. 2021

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In order to improve the global search ability of K-means algorithm and the clustering effect, a K-means method based on the approximate backbone and the shuffled frog leaping algorithm was proposed. Firstly, the classic iterative formula of the K-means algorithm is replaced by the classic shuffled frog leaping algorithm to obtain better clustering results. Secondly, the K-means algorithm based on the approximate backbone and the shuffled frog leaping algorithm is used for the obtained clustering results. Instead of searching for cluster centers, the cluster division is directly modified. Finally, the experimental results on the UCI dataset show that, the running time of the improved clustering algorithm is shorter than that based on the shuffled frog leaping algorithm only, and clustering results obtained by using the improved clustering algorithm are better than those of other algorithms. In addition, the paper uses the improved clustering algorithm to preprocess medical fundus images to optimize the effect of vascular cutting.

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“…Nguyen et al assume that the vessels are line-like structures, and propose to use the multi-scale line filter responses to measure the vesselness [6]. Inspired by [6], improved line detectors are proposed for vessel segmentation in [7,8]. Based on the observation that the profile of the vessels are Gaussian-shaped and symmetric with respect to the peak position, Zhang et al propose to use the responses of the first-order of derivative of Gaussian to measure the vesselness [9].…”

Section: Introductionmentioning

confidence: 99%

Ground truth free retinal vessel segmentation by learning from simple pixels

Zou

Chen

et al. 2020

IET Image Processing

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Retinal vessel segmentation is fundamental for the automatic retinal image analysis and ocular disease screening. This paper aims to learn a ground truth free feature aggregation strategy for the vessel segmentation. Five vesselness maps modelling the vessels'profile, appearance, and shape are first generated. Together, the histogram of the local binary pattern and the green colour are extracted. In each vesselness map, the pixels with large vesselness values are regarded as simple positive samples. The pixels with small vesselness values are regarded as simple negative samples, and the pixels with mediocre values are treated as difficult pixels. The simple positive samples and simple negative samples near the difficult pixels consist of the training dataset while the rest vesselness maps together with the local binary pattern histogram, and green colour channel are used as the features to learn a strong classifier. Then, without leveraging any ground truth, multiple kernel boosting is used to combine four support vector machine kernels to learn a strong vessel model for each image. Applying this learnt model to the pixels with mediocre values in the single vesselness map, their label will be determined. Totally, five strong vessel models are learnt. Finally, pixels with the majority supports from the strong vessel models are labelled as vessel pixels. The proposed method achieves accuracy of 94.83%, sensitivity of 72.59%, and specificity of 98.11% on DRIVE dataset, and accuracy of 95.51%, sensitivity of 78.09%, and specificity of 97.56% on STARE. It outperforms the state‐of‐the‐art unsupervised methods and achieves comparable performances to the supervised methods.

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A new robust method for blood vessel segmentation in retinal fundus images based on weighted line detector and hidden Markov model

Cited by 52 publications

References 45 publications

Retinal Vessel Automatic Segmentation Using SegNet

Retinal Vessel Automatic Segmentation Using SegNet

An Improved SFLA-Kmeans Algorithm Based on Approximate Backbone and Its Application in Retinal Fundus Image

Ground truth free retinal vessel segmentation by learning from simple pixels

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