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.

在高强度聚焦超声 (HIFU) 治疗中，准确识别变性和正常生物组织至关重要。本文提出了一种基于压缩感知（CS）和精细复合多尺度模糊熵（RCMFE）的新方法。首先，CS 用于对 HIFU 回波信号进行去噪。然后计算去噪HIFU回波信号的多尺度模糊熵(MFE)和RCMFE。本研究分析了 90 例 HIFU 回波信号，其中正常状态 45 例，变性状态 45 例，结果表明，虽然 MFE 和 RCMFE 均可用于识别变性组织，但 RCMFE 在每个比例因子小于MFE，类间距离大于MFE。与 MFE 相比，RCMFE 可以更准确地计算信号的复杂度，提高稳定性、紧凑性和可分离性。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. 当比例因子选择为 16 时，可以获得最好的区分效果。