Purpose: A novel image-based method for speed of sound (SoS) estimation is proposed and experimentally validated on a tissue-mimicking ultrasound phantom and normal human liver in vivo using linear and curved array tr...Purpose: A novel image-based method for speed of sound (SoS) estimation is proposed and experimentally validated on a tissue-mimicking ultrasound phantom and normal human liver in vivo using linear and curved array transducers. Methods: When the beamforming SoS settings are adjusted to match the real tissue’s SoS, the ultrasound image at regions of interest will be in focus and the image quality will be optimal. Based on this principle, both a tissue-mimicking ultrasound phantom and normal human liver in vivo were used in this study. Ultrasound image was acquired using different SoS settings in beamforming channels ranging from 1420 m/sec to 1600 m/sec. Two regions of interest (ROIs) were selected. One was in a fully developed speckle region, while the other contained specular reflectors. We evaluated the image quality of these two ROIs in images acquired at different SoS settings in beamforming channels by using the normalized autocorrelation function (ACF) of the image data. The values of the normalized ACF at a specific lag as a function of the SoS setting were computed. Subsequently, the soft tissue’s SoS was determined from the SoS setting at the minimum value of the normalized ACF. Results: The value of the ACF as a function of the SoS setting can be computed for phantom and human liver images. SoS in soft tissue can be determined from the SoS setting at the minimum value of the normalized ACF. The estimation results show that the SoS of the tissue-mimicking phantom is 1460 m/sec, which is consistent with the phantom manufacturer’s specification, and the SoS of the normal human liver is 1540 m/sec, which is within the range of the SoS in a healthy human liver in vivo. Conclusion: Soft tissue’s SoS can be determined by analyzing the normalized ACF of ultrasound images. The method is based on searching for a minimum of the normalized ACF of ultrasound image data with a specific lag among different SoS settings in beamforming channels.展开更多
In spite of the advancement in computerized imaging,many image modalities produce images with commotion influencing both the visual quality and upsetting quantitative image analysis.In this way,the research in the zone...In spite of the advancement in computerized imaging,many image modalities produce images with commotion influencing both the visual quality and upsetting quantitative image analysis.In this way,the research in the zone of image denoising is very dynamic.Among an extraordinary assortment of image restoration and denoising techniques the neural network system-based noise sup-pression is a basic and productive methodology.In this paper,Bilateral Filter(BF)based Modular Neural Networks(MNN)has been utilized for speckle noise sup-pression in the ultrasound image.Initial step the BFfilter is used tofilter the input image.From the output of BF,statistical features such as mean,standard devia-tion,median and kurtosis have been extracted and these features are used to train the MNN.Then,thefiltered images from the BF are again denoised using MNN.The ultrasound dataset from the Kaggle site is used for the training and testing process.The simulation outcomes demonstrate that the BF-MNNfiltering method performs better for the multiplicative noise concealment in UltraSound(US)images.From the simulation results,it has been observed that BF-MNN performs better than the existing techniques in terms of peak signal to noise ratio(34.89),Structural Similarity Index(0.89)and Edge Preservation Index(0.67).展开更多
文摘Purpose: A novel image-based method for speed of sound (SoS) estimation is proposed and experimentally validated on a tissue-mimicking ultrasound phantom and normal human liver in vivo using linear and curved array transducers. Methods: When the beamforming SoS settings are adjusted to match the real tissue’s SoS, the ultrasound image at regions of interest will be in focus and the image quality will be optimal. Based on this principle, both a tissue-mimicking ultrasound phantom and normal human liver in vivo were used in this study. Ultrasound image was acquired using different SoS settings in beamforming channels ranging from 1420 m/sec to 1600 m/sec. Two regions of interest (ROIs) were selected. One was in a fully developed speckle region, while the other contained specular reflectors. We evaluated the image quality of these two ROIs in images acquired at different SoS settings in beamforming channels by using the normalized autocorrelation function (ACF) of the image data. The values of the normalized ACF at a specific lag as a function of the SoS setting were computed. Subsequently, the soft tissue’s SoS was determined from the SoS setting at the minimum value of the normalized ACF. Results: The value of the ACF as a function of the SoS setting can be computed for phantom and human liver images. SoS in soft tissue can be determined from the SoS setting at the minimum value of the normalized ACF. The estimation results show that the SoS of the tissue-mimicking phantom is 1460 m/sec, which is consistent with the phantom manufacturer’s specification, and the SoS of the normal human liver is 1540 m/sec, which is within the range of the SoS in a healthy human liver in vivo. Conclusion: Soft tissue’s SoS can be determined by analyzing the normalized ACF of ultrasound images. The method is based on searching for a minimum of the normalized ACF of ultrasound image data with a specific lag among different SoS settings in beamforming channels.
文摘In spite of the advancement in computerized imaging,many image modalities produce images with commotion influencing both the visual quality and upsetting quantitative image analysis.In this way,the research in the zone of image denoising is very dynamic.Among an extraordinary assortment of image restoration and denoising techniques the neural network system-based noise sup-pression is a basic and productive methodology.In this paper,Bilateral Filter(BF)based Modular Neural Networks(MNN)has been utilized for speckle noise sup-pression in the ultrasound image.Initial step the BFfilter is used tofilter the input image.From the output of BF,statistical features such as mean,standard devia-tion,median and kurtosis have been extracted and these features are used to train the MNN.Then,thefiltered images from the BF are again denoised using MNN.The ultrasound dataset from the Kaggle site is used for the training and testing process.The simulation outcomes demonstrate that the BF-MNNfiltering method performs better for the multiplicative noise concealment in UltraSound(US)images.From the simulation results,it has been observed that BF-MNN performs better than the existing techniques in terms of peak signal to noise ratio(34.89),Structural Similarity Index(0.89)and Edge Preservation Index(0.67).
