Name
Abstract | ||
|---|---|---|
Conventional ultrasound imaging technology used the amplitude information in the ultrasonic linear signal to form grayscale images. Doctors identified the tissues in grayscale images by their structures. Due to the requirement of specific positions and orientations of the ultrasonic probe, this method relied heavily on the doctor's experience and resulted in a high misdiagnosis rate. Compared with ultrasound linear signal, the nonlinear signal contained more information such as frequency and phase that can be used in ultrasound imaging. So we proposed a spectral-based color ultrasound imaging technique based on nonlinear vibration in this paper. First, we used wavelet transform to analyze spectral features. Then, the spectral features were analyzed by principal component analysis to reduce the dimension. Finally, different tissues with different spectral features were color-coded according to the projected coordinates in the eigenspace formed by the principal components, thereby realizing the use of feature colors to distinguish various biological tissues. The experimental results demonstrated that different tissues could be recognized clearly by feature colors, which verified the feasibility of this technique. This technique pioneers the way to ultrasound diagnosis without the necessity of specific positions or orientations of ultrasound probes, reducing the complexity and promoting the efficiency significantly. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/IST.2018.8577086 | 2018 IEEE International Conference on Imaging Systems and Techniques (IST) |
Keywords | Field | DocType |
Nonlinear ultrasound,spectral patterns,wavelet transform,principal component analysis,color ultrasound imaging | Computer vision,Ultrasonic sensor,Nonlinear system,Pattern recognition,Computer science,Spectral line,Artificial intelligence,Eigenvalues and eigenvectors,Grayscale,Principal component analysis,Ultrasound,Wavelet transform | Conference |
ISSN | ISBN | Citations |
1558-2809 | 978-1-5386-6629-6 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianguo Ma | 1 | 6 | 2.31 |
| Zhun Xie | 2 | 0 | 0.34 |
| Wei Min | 3 | 6 | 5.17 |
| Lijun Xu | 4 | 85 | 44.81 |
| Boya Chen | 5 | 0 | 0.34 |
| Yulin Li | 6 | 15 | 3.28 |
| Jie Du | 7 | 0 | 0.34 |
| Zijie Fang | 8 | 0 | 0.34 |