Abstract
Recognition of biological tissue denaturation is a vital work in high-intensity focused ultrasound (HIFU) therapy. Multiscale permutation entropy (MPE) is a nonlinear signal processing method for feature extraction, widely applied to the recognition of biological tissue denaturation. However, the typical MPE cannot derive a stable entropy due to intensity information loss during the coarse-graining process. For this problem, an improved multiscale permutation entropy (IMPE) is proposed in this work. IMPE is obtained through refining and reconstructing MPE. Compared with MPE, the IMPE overcomes the deficiency of amplitude information loss due to the coarse-graining process when computing signal complexity. Through the simulation of calculating MPE and IMPE from white Gaussian noise, it is found that the entropy derived by IMPE is more stable than that derived by MPE. The processing method based on IMPE feature extraction is applied to the experimental ultrasonic scattered echo signals in HIFU treatment. Support vector machine and Gustafson–Kessel fuzzy clustering based on MPE and IMPE feature extraction are also used for biological tissue denaturation classification and recognition. The results calculated from the different combination algorithms show that the recognition of biological tissue denaturation based on IMPE-GK clustering is more reliable with the accuracy of 95.5%.
Highlights
High-intensity focused ultrasound (HIFU) is a new technique for tumor therapy with safety, high efficiency, and non-invasive [1–5]
The results show that improved multiscale permutation entropy (IMPE), containing the amplitude information lost in Multiscale permutation entropy (MPE), Thanks to the processing of averaging probability and entropy value, IMPE has goo is more sensitive to the complexity of time series
Compared with MPE, IMPE has better tightness and separability, which means feature extraction based on IMPE can better identify biological tissue denaturation
Summary
High-intensity focused ultrasound (HIFU) is a new technique for tumor therapy with safety, high efficiency, and non-invasive [1–5]. To obtain the characteristic parameters reflecting the temperature and denaturation of tissue in HIFU therapy with ultrasound monitoring, researchers studied the ultrasonic signals from many aspects, such as echo energy, sound attenuation coefficient, frequency offset, and sound velocity [14–19]. Accurate feature extraction based on a nonlinear model and recognition of biological tissue denaturation is the essential works in applying ultrasound monitoring in HIFU therapy. SVM has the advantage of simple structure, fast learning speed, and wide applicability It has been applied in ultrasonic based biological tissue denaturation recognition [38]. SVM and GK fuzzy clustering are used to identify denatured and nondenatured tissues according to the two features of ultrasonic scattered echo extracted by the MPE and IMPE algorithm, respectively.
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