基于压缩感知的医学图像采样新方法研究

压缩感知基于信号的稀疏性或可压缩性,能够以低于奈奎斯特采样率的频率进行采样,从而直接获得压缩后的采样信号。本文将压缩感知的思想应用于医学图像,对已获得的图像进行压缩采样,以降低医学图像的存储空间。基于正交匹配追踪思想,提出分块双阈值正交匹配追踪方法,根据图像不同区域信息量的不同,采取分块处理并加入采样阈值,针对不同子图像块采取不同采样率,提高了采样效率;同时,在重构时加入判断阈值,可降低重构效果对采样阈值的依赖,减小了由于图像的特殊性或人为经验不足而使得采样阈值选取不当对图像重建质量的影响。实验表明,本文方法能够以较低的采样率实现较高的重构精度。

Research on a new method of medical image sampling based on compressed sensing

In practical applications of medical imaging systems,images with high resolution are always needed.However,this will lead to large data that makes it difficult to store a nd transfer.Motivated by the idea of compressed sensing,a nonlinear sampling theory can be used to reconstru ct signal from incomplete frequency data.This paper tries to find out a better sampling me thod to gain high resolution images as well as reduce the data storage.Based on the sparsity or compressibil ity,signals can be sampled by the rate lower than Nyquist sampling rate.Compressed sensing is appli ed in medical image compressive sampling and a new method called partitioning orthogonal matching pursuit with dual threshold s is proposed in this paper.Due to the difference in blocks information quantity,the information quant it y is estimated by sampling threshold to select different sampling rates for different blocks,which can imp rove the sampling and reconstruction efficiency.In order to obtain good reconstruction quality,a jud ging threshold is used to reduce the dependence on the sampling threshold.The experimental results show th at this method can reconstruct an image with higher accuracy and lower samplin g rate.