Although ultrasound imaging has been widely applied in medical diagnosis for decades, the data processing remains primitive. Traditional B-mode ultrasound imaging exhibits the amplitude of the scattered ultrasonic signals as brightness of the images, neglecting rich information delivered by the frequency modulation of the signals. While the doctors diagnose upon the whole image with their experiences, the limited local information poses significant difficulties on computer diagnosis.
Ultrasonic harmonic imaging employs multiple frequency bands in the imaging strategy, indicating that the frequency variation in the spectrum contains nonlinear vibrations which are specific for given biological tissues. We hypothesize that detailed analysis and characterization of the spectrum enable the software to recognize the signals from different organs or from diseased regions. Wavelet transform was utilized to exhibit the ultrasonic signal in both time and frequency domain, followed by the principal component analysis which extracted the feature of the frequency. Pseudo colors, red, green, and blue, were associated with the first 3 principal components as a colorized augmentation of the ultrasound imaging.
Results and Conclusion:
In the preliminary test, each pixel of the image distinguished itself by frequency characteristics in the wavelet transform. Principal component analysis recognized the major characteristics and presented them in pseudo color images. The hypodermic layers, the kidney, and the surrounding tissues distinguished themselves clearly from one another by the color association. The ultrasonic spectral analysis and augmented visualization technique pioneered the way to intelligent ultrasonic imaging systems and computer-aided diagnosis.
Jianguo Ma, Boya Chen, Min Wei, Yulin Li, Jie Du, and Lijun Xu, "Ultrasonic spectral analysis and augmented visualization for medical imaging (Conference Presentation)," Proc. SPIE 10600, Health Monitoring of Structural and Biological Systems XII, 1060010 (Presented at SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring: March 07, 2018; Published: 3 April 2018); https://doi.org/10.1117/12.2297147.5763102548001.
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