Ultrasound (US) has become one of the most commonly performed imaging modalities in clinical practice. It is a rapidly evolving technology with certain advantages and with unique challenges that include low imaging qu...Ultrasound (US) has become one of the most commonly performed imaging modalities in clinical practice. It is a rapidly evolving technology with certain advantages and with unique challenges that include low imaging quality and high variability. From the perspective of image analysis, it is essential to develop advanced automatic US image analysis methods to assist in US diagnosis and/or to make such assessment more objective and accurate. Deep learning has recently emerged as the leading machine learning tool in various research fields, and especially in general imaging analysis and computer vision. Deep learning also shows huge potential for various automatic US image analysis tasks. This review first briefly introduces several popular deep learning architectures, and then summarizes and thoroughly discusses their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation. Finally, the open challenges and potential trends of the future application of deep learning in medical US image analysis are discussed.展开更多
We propose a new multipurpose audio watermarking scheme in which two complementary watermarks are used. For audio copyright protection, the watermark data with copyright information or signature are first encrypted by...We propose a new multipurpose audio watermarking scheme in which two complementary watermarks are used. For audio copyright protection, the watermark data with copyright information or signature are first encrypted by Arnold transforma- tion. Then the watermark data are inserted in the low frequency largest significant discrete cosine transform （DCT） coefficients to obtain robustness performance. For audio authentication, a chaotic signal is inserted in the high frequency insignificant DCT coefficients to detect tampered regions. Furthermore, the synchronization code is embedded in the audio statistical characteristics to resist desynchronization attacks. Experimental results show that our proposed method can not only obtain satisfactory detection and tampered location, but also achieve imperceptibility and robustness to common signal processing attacks, such as cropping, shifting, and time scale modification （TSM）. Comparison results show that our method outperforms some existing methods.展开更多
基金the National Natural Science Foundation of China (61571304, 81571758, and 61701312)the National Key Research and Development Program of China (2016YFC0104703)+1 种基金the Medical Scientific Research Foundation of Guangdong Province, China (B2018031)the Shenzhen Peacock Plan (KQTD2016053112051497).
文摘Ultrasound (US) has become one of the most commonly performed imaging modalities in clinical practice. It is a rapidly evolving technology with certain advantages and with unique challenges that include low imaging quality and high variability. From the perspective of image analysis, it is essential to develop advanced automatic US image analysis methods to assist in US diagnosis and/or to make such assessment more objective and accurate. Deep learning has recently emerged as the leading machine learning tool in various research fields, and especially in general imaging analysis and computer vision. Deep learning also shows huge potential for various automatic US image analysis tasks. This review first briefly introduces several popular deep learning architectures, and then summarizes and thoroughly discusses their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation. Finally, the open challenges and potential trends of the future application of deep learning in medical US image analysis are discussed.
文摘We propose a new multipurpose audio watermarking scheme in which two complementary watermarks are used. For audio copyright protection, the watermark data with copyright information or signature are first encrypted by Arnold transforma- tion. Then the watermark data are inserted in the low frequency largest significant discrete cosine transform （DCT） coefficients to obtain robustness performance. For audio authentication, a chaotic signal is inserted in the high frequency insignificant DCT coefficients to detect tampered regions. Furthermore, the synchronization code is embedded in the audio statistical characteristics to resist desynchronization attacks. Experimental results show that our proposed method can not only obtain satisfactory detection and tampered location, but also achieve imperceptibility and robustness to common signal processing attacks, such as cropping, shifting, and time scale modification （TSM）. Comparison results show that our method outperforms some existing methods.
