熵(时间箭头)
模糊逻辑
计算机科学
压缩传感
Echo(通信协议)
复合数
比例(比率)
超声波
生物系统
模式识别(心理学)
数学
人工智能
生物医学工程
声学
算法
物理
医学
生物
热力学
量子力学
计算机网络
作者
Shang-Qu Yan,Han Zhang,Bei Liu,Michael B. Hall,Shengyou Qian
出处
期刊:Chinese Physics B
[IOP Publishing]
日期:2021-02-01
卷期号:30 (2): 028704-028704
被引量:5
标识
DOI:10.1088/1674-1056/abcfa7
摘要
In high intensity focused ultrasound (HIFU) treatment, it is crucial to accurately identify denatured and normal biological tissues. In this paper, a novel method based on compressed sensing (CS) and refined composite multi-scale fuzzy entropy (RCMFE) is proposed. First, CS is used to denoise the HIFU echo signals. Then the multi-scale fuzzy entropy (MFE) and RCMFE of the denoised HIFU echo signals are calculated. This study analyzed 90 cases of HIFU echo signals, including 45 cases in normal status and 45 cases in denatured status, and the results show that although both MFE and RCMFE can be used to identify denatured tissues, the intra-class distance of RCMFE on each scale factor is smaller than MFE, and the inter-class distance is larger than MFE. Compared with MFE, RCMFE can calculate the complexity of the signal more accurately and improve the stability, compactness, and separability. When RCMFE is selected as the characteristic parameter, the RCMFE difference between denatured and normal biological tissues is more evident than that of MFE, which helps doctors evaluate the treatment effect more accurately. When the scale factor is selected as 16, the best distinguishing effect can be obtained.
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