International Journal of Medical Imaging

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A BP Neural Networks Algorithm for High Resolution of Biomedical Modeling and Image Segmentation

Received: 23 October 2016    Accepted: 07 November 2016    Published: 29 December 2016
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Abstract

High resolution of image segment algorithm plays a very important role in biomedical modeling and diagnosis, which is difficult to be easily solved by traditional algorithms. This article presents a biomedical image segment algorithm based on computational intelligence. First, an assessment method for image resolution is proposed here, and some related models are also compared. In addition, the assessment method aims at high resolution, rather than defining a comprehensive model of the human visual system. Second, a high resolution algorithm is illustrated where the BP neural network is trained from numerical features. The proposed approach permits person to get biomedical model with a high resolution. Third, some experimental results are presented for illustration, and the numerical analysis verifies the resolution measurement and the effectiveness of the BP neural method. Last, some interesting conclusions and future work are indicated at the end of the paper.

DOI 10.11648/j.ijmi.20160406.13
Published in International Journal of Medical Imaging (Volume 4, Issue 6, November 2016)
Page(s) 57-69
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

High Resolution, Bio Modeling, Image Segment, Neural Networks

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Author Information
  • College of Computer and Information Technology, China Three Gorges University, Yichang, China

  • School of Law and Public Administration, China Three Gorges University, Yichang, China

  • College of Computer and Information Technology, China Three Gorges University, Yichang, China

  • College of Computer and Information Technology, China Three Gorges University, Yichang, China

Cite This Article
  • APA Style

    Zheng Xiang, Hui Xie, Junhao Li, Zhengying Cai. (2016). A BP Neural Networks Algorithm for High Resolution of Biomedical Modeling and Image Segmentation. International Journal of Medical Imaging, 4(6), 57-69. https://doi.org/10.11648/j.ijmi.20160406.13

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    ACS Style

    Zheng Xiang; Hui Xie; Junhao Li; Zhengying Cai. A BP Neural Networks Algorithm for High Resolution of Biomedical Modeling and Image Segmentation. Int. J. Med. Imaging 2016, 4(6), 57-69. doi: 10.11648/j.ijmi.20160406.13

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    AMA Style

    Zheng Xiang, Hui Xie, Junhao Li, Zhengying Cai. A BP Neural Networks Algorithm for High Resolution of Biomedical Modeling and Image Segmentation. Int J Med Imaging. 2016;4(6):57-69. doi: 10.11648/j.ijmi.20160406.13

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  • @article{10.11648/j.ijmi.20160406.13,
      author = {Zheng Xiang and Hui Xie and Junhao Li and Zhengying Cai},
      title = {A BP Neural Networks Algorithm for High Resolution of Biomedical Modeling and Image Segmentation},
      journal = {International Journal of Medical Imaging},
      volume = {4},
      number = {6},
      pages = {57-69},
      doi = {10.11648/j.ijmi.20160406.13},
      url = {https://doi.org/10.11648/j.ijmi.20160406.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijmi.20160406.13},
      abstract = {High resolution of image segment algorithm plays a very important role in biomedical modeling and diagnosis, which is difficult to be easily solved by traditional algorithms. This article presents a biomedical image segment algorithm based on computational intelligence. First, an assessment method for image resolution is proposed here, and some related models are also compared. In addition, the assessment method aims at high resolution, rather than defining a comprehensive model of the human visual system. Second, a high resolution algorithm is illustrated where the BP neural network is trained from numerical features. The proposed approach permits person to get biomedical model with a high resolution. Third, some experimental results are presented for illustration, and the numerical analysis verifies the resolution measurement and the effectiveness of the BP neural method. Last, some interesting conclusions and future work are indicated at the end of the paper.},
     year = {2016}
    }
    

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  • TY  - JOUR
    T1  - A BP Neural Networks Algorithm for High Resolution of Biomedical Modeling and Image Segmentation
    AU  - Zheng Xiang
    AU  - Hui Xie
    AU  - Junhao Li
    AU  - Zhengying Cai
    Y1  - 2016/12/29
    PY  - 2016
    N1  - https://doi.org/10.11648/j.ijmi.20160406.13
    DO  - 10.11648/j.ijmi.20160406.13
    T2  - International Journal of Medical Imaging
    JF  - International Journal of Medical Imaging
    JO  - International Journal of Medical Imaging
    SP  - 57
    EP  - 69
    PB  - Science Publishing Group
    SN  - 2330-832X
    UR  - https://doi.org/10.11648/j.ijmi.20160406.13
    AB  - High resolution of image segment algorithm plays a very important role in biomedical modeling and diagnosis, which is difficult to be easily solved by traditional algorithms. This article presents a biomedical image segment algorithm based on computational intelligence. First, an assessment method for image resolution is proposed here, and some related models are also compared. In addition, the assessment method aims at high resolution, rather than defining a comprehensive model of the human visual system. Second, a high resolution algorithm is illustrated where the BP neural network is trained from numerical features. The proposed approach permits person to get biomedical model with a high resolution. Third, some experimental results are presented for illustration, and the numerical analysis verifies the resolution measurement and the effectiveness of the BP neural method. Last, some interesting conclusions and future work are indicated at the end of the paper.
    VL  - 4
    IS  - 6
    ER  - 

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