Volume 4, Issue 6, November 2016, Page: 57-69
A BP Neural Networks Algorithm for High Resolution of Biomedical Modeling and Image Segmentation
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
Received: Oct. 23, 2016;       Accepted: Nov. 7, 2016;       Published: Dec. 29, 2016
DOI: 10.11648/j.ijmi.20160406.13      View  2481      Downloads  74
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.
Keywords
High Resolution, Bio Modeling, Image Segment, Neural Networks
To cite this article
Zheng Xiang, Hui Xie, Junhao Li, Zhengying Cai, A BP Neural Networks Algorithm for High Resolution of Biomedical Modeling and Image Segmentation, International Journal of Medical Imaging. Vol. 4, No. 6, 2016, pp. 57-69. doi: 10.11648/j.ijmi.20160406.13
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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