Recognition of denatured biological tissue based on variational mode decomposition and multi-scale permutation entropy

Abstract

It is an important practical problem to accurately recognize whether biological tissue is denatured during high intensity focused ultrasound (HIFU) treatment. Ultrasonic scattering echo signals are related to some physical properties of biological tissues. According to the characteristics of ultrasonic scattering echo signals, the recognition of denatured biological tissues is studied based on the variational mode decomposition (VMD) and multi-scale permutation entropy (MPE) in this paper. The ultrasonic echo signals are decomposed into various modal components by the VMD. The noise components and the useful components are separated according to the power spectrum information entropy of various modal components. The separated useful signals are reconstructed and the MPE are extracted. Furthermore, Gustafson-Kessel (GK) fuzzy clustering analysis is employed to obtain the standard clustering center, and the recognition of denatured biological tissues is carried out by Euclid approach degree and principle of proximity. The proposed method is applied to ultrasonic scattering echo signal during HIFU treatment. In order to determine the parameters of MPE algorithm for ultrasonic scattering echo signals, the embedding dimension of the MPE is discussed, and the scale factor of the MPE algorithm is optimized by genetic algorithm. When the delay time and the embedding dimension are 2 and 7 respectively, the MPE values decrease with scale factor increasing. Assuming that the scale factor is 12 from optimization results, the 293 ultrasonic scattering echo signals from normal tissues and denatured tissues are analyzed by the MPE. It is found that the MPE values of the denatured tissues are higher than those of the normal tissues. The MPE can be used to distinguish normal tissues and denatured tissues. Comparing with the recognition methods of the EMD-MPE-GK fuzzy clustering method and the VMD-WE-GK fuzzy clustering, the proposed method has good clustering performance and separability. Its partition coefficient (PC) is close to 1 and the Xie-Beni (XB) index is smaller. There are fewer feature points in the overlap region between MPE features of denatured tissues and normal tissues. The recognition results of denatured biological tissues in this experimental environment show that the recognition rate based on this method is higher, reaching up to 93.81%.

Study on Optimization of Infrasound Filtering Method for Coal Sample Failure under Load

“…At present, the commonly used signal filtering methods include wavelet analysis, wavelet packet analysis, EMD analysis, and so on. Among them, wavelet analysis and EMD are the most widely used (Dragomiretskiy and Zosso, 2014;Tang and Wang, 2015;Liu et al, 2019;Wang et al, 2020). A large number of environmental noise signals were included in the test signals of this study, and it was difficult to achieve better filtering effects using band-pass filtering methods.…”

Biological Tissue Damage Monitoring Method Based on IMWPE and PNN during HIFU Treatment

“…The subtraction images of the irradiation area are shown in Figure 5. According to the reference [23,26], the actual damaged status of biological tissues were determined by the temperature and subtraction image of the irradiation area. The porcine muscle tissue before HIFU irradiation is normal tissue, and the tissue subtraction image shows that there is no white bright block in the irradiated area according to Figure 5a.…”

“…Compared with the Shannon entropy and MSE mentioned above, multi-scale permutation entropy (MPE) has the advantages including strong noise resistance and robustness, so the MPE is often employed to analyze the complexity of ultrasonic time series [20][21][22]. In [23], MPE, as the characteristic of the ultrasonic scattering echo signal, was used to distinguish the nondenatured biological tissue and denatured biological tissue during HIFU treatment. However, MPE also has some shortcomings, such as, it loses information about the signal amplitude due to that MPE does not analyze the amplitude difference between the same ordinal modes in analyzing signal complexity.…”

Recognition of Biological Tissue Denaturation Based on Improved Multiscale Permutation Entropy and GK Fuzzy Clustering

“…MPE is widely used in bearing damage diagnosis and other industrial fields [30,31]. In our previous work, MPE and its variants have been applied to recognition of tissue denaturation in HIFU therapy [32,33]. However, MPE also has a defect due to the coarse-graining process, which results in the amplitude information loss in the same permutation pattern.…”