Method of medical ultrasonic image de-noising based on fuzzy PCNN in the wavelet domain

被引:0
|
作者
Wang, Shao-Bo [1 ]
Guo, Ye-Cai [2 ,3 ]
Gao, Min [3 ]
Liu, Zhen-Xing [3 ]
Zhao, Xue-Qing [3 ]
机构
[1] College of Medical Anhui University of Science and Technology, Huainan 232001, China
[2] College of Electronic and Information Science and Technology, Nanjing University of Information Engineering, Nanjing 210044, China
[3] School of Electrical Engineering and Information, Anhui University of Science and Technology, Huainan 232001, China
来源
Guangdianzi Jiguang/Journal of Optoelectronics Laser | 2010年 / 21卷 / 03期
关键词
Fuzzy sets - Speckle - Ultrasonic imaging - Image denoising - Medical imaging - Neural networks;
D O I
暂无
中图分类号
学科分类号
摘要
Based on the analysis of the speckle noise and PCNN's properties, PCNN is introduced in to wavelet domain, by combining the thought of the Soft-threshold de-noising, Soft-threshold de-noising method of medical ultrasonic image based on PCNN(ST-PCNN) is proposed. The advantage of ST-PCNN is that PCNN recognizes the coefficients of high frequency in wavelet domain are realized, and then the wavelet coefficients are processed by corresponding methods. ST-PCNN improves the disadvantage that PCNN can not accurately determine the position of speckle noise and the fixed threshold makes some high-frequency signals loss, and better reserves the wavelet coefficients of high frequency signal which are lower than the fixed threshold. On this basis, fuzzy algorithm is applied in the model of PCNN, method of medical ultrasonic image de-noising based on Fuzzy PCNN in the Wavelet Domain (F-PCNN-WD) is proposed. The proposed method make use of fuzzy algorithm to remove the wavelet coefficients of speckle noise which are greater than the ignition threshold value of PCNN, so the speckle noise can be better removed. The experimental results show that ST-PCNN and F-PCNN-WD can not only remove the noise but also reserve the detail information and the image edge.
引用
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页码:476 / 480
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