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Even faster retinal vessel segmentation via accelerated singular value decomposition

  • Deep Learning & Neural Computing for Intelligent Sensing and Control
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Abstract

Retinal blood vessel segmentation plays a vital role in medical image analysis since the appearance of vessels would contribute in the diagnosis, treatment, and evaluation for various diseases in ophthalmology and other fields, such as cardiology and neurosurgery. Among the state-of-the-art blood vessel segmentation techniques, the Hessian-based multi-scale filter has been widely used and shown its superior performance in the accuracy and visual effect. However, its execution time still remains a challenge due to the employment of eigenvalue decomposition in this approach. Bearing this in mind, we propose an accelerated matrix decomposition mechanism, which could be used to boost not only the original Hessian-based multi-scale approach but also the singular value decomposition-based algorithms. To evaluate the proposed method, we conducted comparison experiments between state-of-the-art techniques and our method. Experimental results show the superior performance of the proposed approach over state of the arts especially in execution time.

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Availability of data and material

We would like to share our image dataset very much with the public upon getting a permission from the hospital where the dataset was acquired. We will try our best to do it because we think it can facilitate the related fields growth and help on advertising our approach.

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Acknowledgements

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

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This is one of the achievements of enterprise innovation management research institute.

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Authors and Affiliations

  1. College of Industry and Commerce, Shandong Management University, Jinan, 250357, China

    Yan Zhang

  2. School of Light Industry Science and Engineering, Qilu University of Technology (Shandong Academy of Science), Jinan, 250353, China

    Luo Rong

  3. Department of Electrical Engineering Information Technology, Shandong University of Sci&Tech, Jinan, 250031, China

    Yan Zhang, Jian Lian & Chengjiang Li

  4. School of Information Science and Engineering, Key Lab of Intelligent Computing and Information Security in Universities of Shandong, Institute of Life Sciences, Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, and Key Lab of Intelligent Information Processing, Shandong Normal University, Jinan, 250358, China

    Jian Lian, Weikuan Jia & Yuanjie Zheng

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  1. Yan Zhang
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  4. Weikuan Jia
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Zhang, Y., Lian, J., Rong, L. et al. Even faster retinal vessel segmentation via accelerated singular value decomposition. Neural Comput & Applic 32, 1893–1902 (2020). https://doi.org/10.1007/s00521-019-04505-1

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