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
This study examined how the signals of interest （SOI） effect on the backscattering measurement numerically based on 3-D finite-difference time-domain （FDTD） method. High resolution microstructure mappings of bovin...This study examined how the signals of interest （SOI） effect on the backscattering measurement numerically based on 3-D finite-difference time-domain （FDTD） method. High resolution microstructure mappings of bovine cancellous bones provided by micro-CT were used as the input geometry for simulations. Backscatter coefficient （BSC）, integrated backscatter coefficient （IBC） and apparent integrated backscatter （AIB） were calculated with changing the start （L1） and duration （L2） of the SOl. The results demonstrated that BSC and IBC decrease as L1 increases, and AIB decreases more rapidly as L1 increases. The backscattering parameters increase with fluctuations as a function of L2 when L2 is less than 6 mm. However, BSC and IBC change little as L2 continues to increase, while AIB slowly decreases as L2 continues to increase. The results showed how the selections of the SOI effect on the backscattering measurement. An explicit standard for SOl selection was proposed in this study and short L1 （about 1.5 mm） and appropriate L2 （6 mm-12 mm） were recommended for the calculations of backscattering parameters.展开更多
Improving the diagnosis accuracy is essential for the clinical application of osteoporosis evaluation using ultrasonic backscatter signal.In vitro ultrasonic backscatter signals were measured on bone specimens and bac...Improving the diagnosis accuracy is essential for the clinical application of osteoporosis evaluation using ultrasonic backscatter signal.In vitro ultrasonic backscatter signals were measured on bone specimens and backscatter parameters were calculated.Using the measured backscatter parameters,the involved cancellous bone specimens were evaluated and classified using support vector machine and adaptive boosting algorithms.Results showed that the accuracy of classification was 80.00%-82.86% and the specificity of osteoporosis diagnosis was significant(specificity>92.3%).The supervised machine learning method using ultrasonic backscatter in bone evaluation is effective in the diagnosis of osteoporosis.The performance of the proposed machine-learning method is superior to the traditional bone evaluation using quantitative backscatter parameters.This study may contribute to the application of ultrasonic backscatter in the diagnosis of osteoporosis in vivo.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.
基金supported by the National Natural Science Foundation of China(Grant No. 11174060)the Ph.D. Programs Foundation of the Ministry of Education of China(Grant Nos. 20090071110066,20110071130004)+1 种基金the Key Science and Technology Program of Shanghai(Grant No. 09441900400)the Program for New Century Excellent Talents in University(Grant No. NCET-10-0349)
文摘This study examined how the signals of interest （SOI） effect on the backscattering measurement numerically based on 3-D finite-difference time-domain （FDTD） method. High resolution microstructure mappings of bovine cancellous bones provided by micro-CT were used as the input geometry for simulations. Backscatter coefficient （BSC）, integrated backscatter coefficient （IBC） and apparent integrated backscatter （AIB） were calculated with changing the start （L1） and duration （L2） of the SOl. The results demonstrated that BSC and IBC decrease as L1 increases, and AIB decreases more rapidly as L1 increases. The backscattering parameters increase with fluctuations as a function of L2 when L2 is less than 6 mm. However, BSC and IBC change little as L2 continues to increase, while AIB slowly decreases as L2 continues to increase. The results showed how the selections of the SOI effect on the backscattering measurement. An explicit standard for SOl selection was proposed in this study and short L1 （about 1.5 mm） and appropriate L2 （6 mm-12 mm） were recommended for the calculations of backscattering parameters.
基金supported by the National Natural Science Foundation of China(11804056,11874289,11827808,11525416)Shanghai Talent Development Fund(2018112)+1 种基金Shanghai Science and Technology Innovation Action Plan(19441903400)State Key Laboratory of ASIC and System Project(2018MS004)。
文摘Improving the diagnosis accuracy is essential for the clinical application of osteoporosis evaluation using ultrasonic backscatter signal.In vitro ultrasonic backscatter signals were measured on bone specimens and backscatter parameters were calculated.Using the measured backscatter parameters,the involved cancellous bone specimens were evaluated and classified using support vector machine and adaptive boosting algorithms.Results showed that the accuracy of classification was 80.00%-82.86% and the specificity of osteoporosis diagnosis was significant(specificity>92.3%).The supervised machine learning method using ultrasonic backscatter in bone evaluation is effective in the diagnosis of osteoporosis.The performance of the proposed machine-learning method is superior to the traditional bone evaluation using quantitative backscatter parameters.This study may contribute to the application of ultrasonic backscatter in the diagnosis of osteoporosis in vivo.
