Computer Science ›› 2015, Vol. 42 ›› Issue (11): 299-304.doi: 10.11896/j.issn.1002-137X.2015.11.061

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Compressed Sensing Reconstruction of MRI Images Based on Nonsubsampled Contourlet Transform

CHEN Xiu-mei, WANG Jing-shi, WANG Wei, ZHAO Yang and TANG Min   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Compressed sensing is a newly developed theoretical framework for information acquisition and processing.Using the non-linear optimization methods,the signals can be recovered accurately from fewer linear and non-adaptive measurements by taking advantages of the sparsity or compressibility inherent in real world signals.Compressed sensing represents compressible signals at a sampling rate significantly below the Nyquist rate,and it has been applied in compressive imaging and medical image processing.The flow chart of our algorithm is as follows.The nonsubsampled con-tourlet transform is applied to sparsely represent the original image,then the iterative soft-thresholding algorithm is used to reconstruct the medical MRI images based on Fourier matrix as the measurement matrix.The peak signal to noise rate (PSNR),mutual information (MI) and artifacts power (AP) are used to compare the reconstruction effects of wavelet transform(WT),sharp frequency localization contourlet transform (SFLCT) and nonsubsampled contourlet transform (NSCT).Experimental results demonstrate that our method is superior to the other two methods in PSNR,remained proportion of original information and reconstruction precision.Our algorithm can be extended and widely used in rapid medical imaging technology.

Key words: Compressed sensing,Nonsubsampled contourlet transform,Image reconstruction,Medical image,MRI

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