Super resolution reconstruction for medical image based on adaptive multi-dictionary learning and structural self-similarity.

IF 1.5 4区 医学 Q3 SURGERY
Fang Zhang,Yue Wu,Zhitao Xiao,Lei Geng,Jun Wu,Jia Wen,Wen Wang,Ping Liu
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引用次数: 0

Abstract

To improve the quality of the super-resolution (SR) reconstructed medical images, an improved adaptive multi-dictionary learning method is proposed, which uses the combined information of medical image itself and the natural images database. In training dictionary section, it uses the upper layer images of pyramid which are generated by the self-similarity of low resolution images. In reconstruction section, the top layer image of pyramid is taken as the initial reconstruction image, and medical image's SR reconstruction is achieved by regularization term which is the non-local structure self-similarity of the image. This method can make full use of the same scale and different scale similar information of medical images. Simulation experiments are carried out on natural images and medical images, and the experimental results show the proposed method is effective for improving the effect of medical image SR reconstruction.
基于自适应多字典学习和结构自相似的医学图像超分辨率重建。
为了提高重建的超分辨率医学图像的质量,提出了一种改进的自适应多字典学习方法,该方法将医学图像本身的信息与自然图像数据库相结合。在训练字典部分,使用低分辨率图像的自相似度生成的金字塔上层图像。在重建部分,以金字塔的顶层图像作为初始重建图像,通过正则化项即图像的非局部结构自相似性来实现医学图像的SR重建。该方法可以充分利用医学图像的相同尺度和不同尺度的相似信息。对自然图像和医学图像进行了仿真实验,实验结果表明,该方法能够有效提高医学图像SR重建的效果。
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来源期刊
Computer Assisted Surgery
Computer Assisted Surgery Medicine-Surgery
CiteScore
2.30
自引率
0.00%
发文量
13
审稿时长
10 weeks
期刊介绍: omputer Assisted Surgery aims to improve patient care by advancing the utilization of computers during treatment; to evaluate the benefits and risks associated with the integration of advanced digital technologies into surgical practice; to disseminate clinical and basic research relevant to stereotactic surgery, minimal access surgery, endoscopy, and surgical robotics; to encourage interdisciplinary collaboration between engineers and physicians in developing new concepts and applications; to educate clinicians about the principles and techniques of computer assisted surgery and therapeutics; and to serve the international scientific community as a medium for the transfer of new information relating to theory, research, and practice in biomedical imaging and the surgical specialties. The scope of Computer Assisted Surgery encompasses all fields within surgery, as well as biomedical imaging and instrumentation, and digital technology employed as an adjunct to imaging in diagnosis, therapeutics, and surgery. Topics featured include frameless as well as conventional stereotactic procedures, surgery guided by intraoperative ultrasound or magnetic resonance imaging, image guided focused irradiation, robotic surgery, and any therapeutic interventions performed with the use of digital imaging technology.
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