Introductions.- Despeckle Filters for Medical Ultrasound Images.- Wavelet and Fast Bilateral Filter Based Despeckling Method for Medical Ultrasound Images.- Despeckle Filtering of Medical Ultrasonic Images Using Wavelet and Guided Filter.- Despeckling Method for Medical Images Based on Wavelet and Trilateral Filter.- Nonsubsampled Shearlet and Guided Filter Based Despeckling Method for Medical Ultrasound Images.
Dr. Ju Zhang obtained his B.S. degree in Mechanical Engineering from Zhejiang University of Technology in 1994, and his Ph.D. degree in Automatic Control Engineering from Zhejiang University in 2005. From 2005 to 2006, he was a visiting scholar at Stuttgart University, Germany. From 2007 to 2010, he was an Associate Professor, and since 2011, he has been a Professor, at the College of Information Engineering, Zhejiang University of Technology. From 2014 to 2015, he was a visiting scholar at Michigan State University, USA. His research interests are chiefly in the areas of medical image signal processing and control, and model predictive control. He has published research work in the Journal of Franklin Institute, International Journal of Control, Ultrasonics, International Journal of Automation Control and System, Asian Journal of Control, Circuits System Signal Process and Biomedical Signal and Control.
Dr. Yun Cheng is an Associate Professor at the Department of Ultrasound and the Associate Director of Research and Education Administration at Zhejiang Hospital. From 2012 to 2013, she worked as a visiting researcher at Echo Lab of SUNY Upstate Medical University, Syracuse, USA, where she conducted research on new technologies for echocardiography. Her academic positions include serving as a Committee Member, Echo Branch, Zhejiang Province Medical Association, and as a Committee Member of the Zhejiang Province Precision Medicine Association. For the past several years, her main focus has been on the clinical application of echocardiography and denoising processing for ultrasound images.
Based upon the research they have conducted over the past decade in the field of denoising processes for medical ultrasonic imaging, in this book, the authors systematically present despeckling methods for medical ultrasonic images. Firstly, the respective methods are reviewed and divided into five categories. Secondly, after introducing some basic mathematical tools such as wavelet and shearlet transforms, the authors highlight five recently developed despeckling methods for medical ultrasonic images. In turn, simulations and experiments for clinical ultrasonic images are presented for each method, and comparison studies with other well-known existing methods are conducted, showing the effectiveness and superiority of the new methods. Students and researchers in the field of signal and image processing, as well as medical professionals whose work involves ultrasonic diagnosis, will greatly benefit from this book. Familiarizing them with the state of the art in despeckling methods for medical ultrasonic images, it offers a useful reference guide for their study and research work.