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Lung Parenchymal Segmentation Algorithm Based on Improved Marker Watershed for Lung CT Images

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Pattern Recognition and Computer Vision (PRCV 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11858))

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Abstract

Watershed algorithm is the basic method of digital image processing. and the most important segmentation technology. The lung CT images of human thoracic cross-section are segmented by watershed algorithm and edge detection in this paper. 400 CT images of human lung contour will be picked from Related data set to label artificially. Then the segmentation results are compared with those randomly selected labeled images to evaluate the performance using with Jaccard Index, dice coefficients and Correlation coefficients. At the same time, by comparing the accuracy between the image of lung segmentation and lung connection, the former reached to 99% and the latter is 98%. The real details can be preserved after segmentation by this segmentation.

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Authors and Affiliations

  1. Nanchang Hangkong University, Nanchang, 330063, Jiangxi, China

    Ying Chen & Ding Wang

Authors
  1. Ying Chen
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  2. Ding Wang
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Corresponding author

Correspondence to Ding Wang .

Editor information

Editors and Affiliations

  1. School of EECS, Peking University, Beijing, China

    Zhouchen Lin

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  4. Xidian University, Xi'an, China

    Guangming Shi

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Institute of Artificial Intelligence, Xi'an Jiaotong University, Xi'an, China

    Nanning Zheng

  7. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  8. Northwestern Polytechnical University, Xi'an, China

    Yanning Zhang

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Chen, Y., Wang, D. (2019). Lung Parenchymal Segmentation Algorithm Based on Improved Marker Watershed for Lung CT Images. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_11

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  • DOI: https://doi.org/10.1007/978-3-030-31723-2_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31722-5

  • Online ISBN: 978-3-030-31723-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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