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Lung CT Image Segmentation Based on Capsule Network

Published: 01 February 2021 Publication History

Abstract

In the CT imaging diagnosis of lung diseases, most rely on the naked eye and empirical judgment of doctors, and the misdiagnosis rate is high. Accurate segmentation and extraction of lung parenchyma and nodules are the key to correct analysis of lung diseases. Traditional segmentation methods cannot achieve good segmentation results. To solve this problem, an image segmentation algorithm based on capsule neural network is proposed. First, Gaussian filtering and down-sampling algorithm are performed on the CT image to zoom and denoise the image. and then the preprocessed image is used as input, and the Capsule is used for segmentation. Finally, the resulting segmented image is passed through Markov Conditional Random Field (MRF) Perform smoothing processing. The experimental results show that the segmentation method used in the article can effectively segment the lung parenchyma, and the algorithm is better than traditional segmentation methods.

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  1. Lung CT Image Segmentation Based on Capsule Network

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    EITCE '20: Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering
    November 2020
    1202 pages
    ISBN:9781450387811
    DOI:10.1145/3443467
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 01 February 2021

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    Author Tags

    1. CRF
    2. CT image
    3. Capsule network
    4. Lung
    5. Segmentation

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    EITCE '20 Paper Acceptance Rate 214 of 441 submissions, 49%;
    Overall Acceptance Rate 508 of 972 submissions, 52%

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