Recognition of Biological Tissue Denaturation Based on Improved Multiscale Permutation Entropy and GK Fuzzy Clustering
Abstract
:1. Introduction
2. Theory
2.1. MPE
2.2. IMPE
2.3. GK Fuzzy Clustering
- (1)
- Initializing the number of clustering c, fuzzy index θ, and the membership matrix U to satisfy Formula (12).
- (2)
- Updating the cluster center vi by Formula (13).
- (3)
- Calculating the covariance matrix of the cluster center Fi.
3. Results
3.1. Simulation with White Gaussian Noise (WGN)
3.2. Analysis of Ultrasonic Scattered Echo Signals
3.3. Discussions
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Entropy | Samplings | |||
---|---|---|---|---|
500 | 1000 | 3000 | 5000 | |
MPE | 0.0917 | 0.0587 | 0.0143 | 0.0119 |
IMPE | 0.0215 | 0.0103 | 0.0049 | 0.0020 |
Recognition Methods | Non-Denatured Tissue | Denatured Tissue | Recognition Rate (%) |
---|---|---|---|
MPE-SVM | 81/100 | 96/100 | 88.5 |
IMPE-SVM | 86/100 | 98/100 | 92.0 |
MPE-GK | 85/100 | 97/100 | 91.0 |
IMPE-GK | 92/100 | 99/100 | 95.5 |
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Peng, Z.; Zhang, X.; Cao, J.; Liu, B. Recognition of Biological Tissue Denaturation Based on Improved Multiscale Permutation Entropy and GK Fuzzy Clustering. Information 2022, 13, 140. https://doi.org/10.3390/info13030140
Peng Z, Zhang X, Cao J, Liu B. Recognition of Biological Tissue Denaturation Based on Improved Multiscale Permutation Entropy and GK Fuzzy Clustering. Information. 2022; 13(3):140. https://doi.org/10.3390/info13030140
Chicago/Turabian StylePeng, Ziqi, Xian Zhang, Jing Cao, and Bei Liu. 2022. "Recognition of Biological Tissue Denaturation Based on Improved Multiscale Permutation Entropy and GK Fuzzy Clustering" Information 13, no. 3: 140. https://doi.org/10.3390/info13030140