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Proceedings Paper

Biomedical images texture detail denoising based on PDE
Author(s): Guan-nan Chen; Jian-ji Pan; Chao Li; Rong Chen; Ju-qiang Lin; Zu-fang Huang
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Paper Abstract

Biomedical images denosing based on Partial Differential Equation are well-known for their good processing results. General denosing methods based on PDE can remove the noises of images with gentle changes and preserve more structure details of edges, but have a poor effectiveness on the denosing of biomedical images with many texture details. This paper attempts to make an overview of biomedical images texture detail denosing based on PDE. Subsequently, Three kinds of important image denosing schemes are introduced in this paper: one is image denosing based on the adaptive parameter estimation total variation model, which denosing the images based on region energy distribution; the second is using G norm on the perception scale, which provides a more intuitive understanding of this norm; the final is multi-scale denosing decomposition. The above methods involved can preserve more structures of biomedical images texture detail. Furthermore, this paper demonstrates the applications of those three methods. In the end, the future trend of biomedical images texture detail denosing Based on PDE is pointed out.

Paper Details

Date Published: 28 October 2009
PDF: 6 pages
Proc. SPIE 7519, Eighth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2009), 75190A (28 October 2009); doi: 10.1117/12.843132
Show Author Affiliations
Guan-nan Chen, Fujian Normal Univ. (China)
Huazhong Univ. of Science and Technology (China)
Jian-ji Pan, Fujian Provincial Tumor Hospital (China)
Chao Li, Fujian Provincial Tumor Hospital (China)
Rong Chen, Fujian Normal Univ. (China)
Xiamen Univ. (China)
Ju-qiang Lin, Fujian Normal Univ. (China)
Zu-fang Huang, Fujian Normal Univ. (China)

Published in SPIE Proceedings Vol. 7519:
Eighth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2009)
Qingming Luo; Lihong V. Wang; Valery V. Tuchin; Pengcheng Li; Ling Fu, Editor(s)

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