医学图像数据压缩中的机器学习方法

Machine Learning Methods in Medical Image Compression

在医学研究中会产生大量需要储存、传输的图像数据,如计算机断层扫描(CT)等.医院要处理海量患者的所有数据相当困难,必须对这些数据进行压缩.随着人工智能技术的发展,应用机器学习的医学图像数据压缩方法成为新的研究热点.文中首先总结了传统医学图像数据压缩方法;然后基于机器学习的医学图像数据压缩方法,对各类方法针对脑部CT、肺部CT等不同医学图像的压缩效果进行对比,并对这些方法在压缩率、算法复杂度、重构质量等方面的优缺点进行系统的总结,指出机器学习和感兴趣区域结合的方法可以在享受有损压缩带来的高压缩率的同时保留重要区域的特征信息,比其他压缩方法更贴合医学数据的压缩要求;最后展望了该领域的未来方向和挑战.

A large amount of image data such as CT that needs storage and transmission is generated in medical research.It is hard for the hospital to handle all data of the numerous patients.Therefore,it is of vital impor-tance to compress these image data.Recently,learning-based medical image compression has become a new research trend with the development of artificial intelligence.Traditional methods in medical data compression are firstly reviewed.Further study in learning-based approaches is made,and the compression performance of these approaches in different medical image data such as brain CT and liver CT are shown.In the meantime,the advantages and disadvantages of these approaches in various aspects such as compression ratio,algorithm complexity and reconstruction quality are systematically summarized.It is pointed out that the combination of learning-based method and ROI-based method achieves high compression ratio brought by lossy compression,while keeping the feature information of the critical regions.Consequently,this approach is much more suit-able for medical image compression than others.Finally,the paper concluded with a discussion of future de-velopment in this field.