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Medical Image Segmentation Algorithm Based on Information Combined Level Set

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Machine Learning for Cyber Security (ML4CS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12488))

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Abstract

Medical images are difficult to observe due to blurred organ and tissue boundaries and uneven grayscale in the area, which interferes with the diagnosis of the disease. In this paper, a level set segmentation method combining information is proposed for the redundant effect caused by the segmentation of organ tissue in a complex environment. Simulation results show that, when the improved algorithm is used to segment medical images with blurred borders and uneven grayscale, although it requires a lot of calculation, it obviously sup- presses the redundancy effect and improves the accuracy and efficiency of the segmentation algorithm.

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Acknowledgements

This paper is funded by the International Exchange Program of Harbin Engineering University for Innovation-oriented Talents Cultivation.

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Authors and Affiliations

  1. Department of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China

    Lin Ye, Liu Qingling & Muhammad Ayoob Dars

Authors
  1. Lin Ye
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  2. Liu Qingling
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  3. Muhammad Ayoob Dars
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Corresponding author

Correspondence to Liu Qingling .

Editor information

Editors and Affiliations

  1. Xidian University, Xi'an, China

    Xiaofeng Chen

  2. Guangzhou University, Guangzhou, China

    Hongyang Yan

  3. Michigan State University, East Lansing, MI, USA

    Qiben Yan

  4. Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science, Thuwal, Saudi Arabia

    Xiangliang Zhang

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Ye, L., Qingling, L., Dars, M.A. (2020). Medical Image Segmentation Algorithm Based on Information Combined Level Set. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12488. Springer, Cham. https://doi.org/10.1007/978-3-030-62463-7_38

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  • DOI: https://doi.org/10.1007/978-3-030-62463-7_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62462-0

  • Online ISBN: 978-3-030-62463-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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