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

Zheng Xiang; Hui Xie; Junhao Li; Zhengying Cai

doi:doi:10.11648/j.ijmi.20160406.13

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

Zheng Xiang, Hui Xie, Junhao Li, Zhengying Cai

Published in International Journal of Medical Imaging (Volume 4, Issue 6)

Received: 23 October 2016 Accepted: 7 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.

Published in	International Journal of Medical Imaging (Volume 4, Issue 6)
DOI	10.11648/j.ijmi.20160406.13
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), 2016. Published by Science Publishing Group

Keywords

High Resolution, Bio Modeling, Image Segment, Neural Networks

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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://article.sciencepublishinggroup.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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Author Information

Zheng Xiang

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

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

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

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

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Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

@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://article.sciencepublishinggroup.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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