Dual-Enhanced Registration for Field of View Ultrasound Sonography

• Main Authors: Zijia Fan, Zhongyang Wang, Junchang Xin, Zhiqiong Wang, Lu Liu, Xia Zhang, Jiren Liu
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Extended field of view ultrasound sonography; gray enhancement; generative adversarial network; super-resolution; image registration
• Online Access: https://ieeexplore.ieee.org/document/9138393/

Extended Field of View Ultrasound Sonography (EFOV-US) uses the existing ultrasound images for image stitching, so as to display the shape and scope of organ occupation and the relationship with surrounding tissues comprehensively. However, there are still some problems in Extended Field of View Ultrasound Sonography, such as matching error and unstable quality of image stitching. In view of these problems, we propose Dual-enhanced EFOV-US method that overcomes the limitation and produces higher quality results. Firstly, the gray enhancement method is used to improve the image contrast and reduce the noise interference. Then the super-resolution method based on the generative adversarial network is used to improve the resolution of the ultrasonic image further and increase the number of feature point matching between stitching images. The high quality ultrasound wide-range image is gotten by stitching and fusing the double enhanced image. The experimental results show that the proposed method is effective and practical.