Ultrasonic guided waves (GWs) can be used to evaluate long bones effectively because of the ability to provide the information of the whole bone. In this study, a joint spectrogram segmentation and ridge-extraction (J...Ultrasonic guided waves (GWs) can be used to evaluate long bones effectively because of the ability to provide the information of the whole bone. In this study, a joint spectrogram segmentation and ridge-extraction (JSSRE) method was proposed to separate multiple modes in long bones. First, the Gabor time-frequency transform was applied to obtain the spectrogram of multimodal signals. Then, a multi-class image segmentation algorithm was used to find the corresponding region of each mode in the spectrogram, including an improved watershed transform and a region growing procedure. Finally, the ridges were extracted and the time domain signals representing individual modes were reconstructed from these ridges in each region. The validations of this method were discussed by simulated multimodal signals with different signal-to-noise ratios (SNR). The correlation coefficients between the original signals without noise and the reconstructed signals were calculated to analyze the results quantitatively. The results showed that the extracted ridges were in good agreement with generated theoretical dispersion curves, and the reconstructed signals were highly related to the original signals, even under the SNR=3 dB situation.展开更多
基金supported by the National Natural Science Foundation of China(Grant No. 11174060)the PhD Programs Foundation of the Ministry of Education of China(Grant Nos. 20090071110066 and 20110071130004)the New Century Excellent Talents of the Ministry of Education of China(Grant No. NCET-10-0349)
文摘Ultrasonic guided waves (GWs) can be used to evaluate long bones effectively because of the ability to provide the information of the whole bone. In this study, a joint spectrogram segmentation and ridge-extraction (JSSRE) method was proposed to separate multiple modes in long bones. First, the Gabor time-frequency transform was applied to obtain the spectrogram of multimodal signals. Then, a multi-class image segmentation algorithm was used to find the corresponding region of each mode in the spectrogram, including an improved watershed transform and a region growing procedure. Finally, the ridges were extracted and the time domain signals representing individual modes were reconstructed from these ridges in each region. The validations of this method were discussed by simulated multimodal signals with different signal-to-noise ratios (SNR). The correlation coefficients between the original signals without noise and the reconstructed signals were calculated to analyze the results quantitatively. The results showed that the extracted ridges were in good agreement with generated theoretical dispersion curves, and the reconstructed signals were highly related to the original signals, even under the SNR=3 dB situation.
