Abstract：Noise and streak artifacts are serious in low-dose CT (LDCT) images of lung,especially for the top and bottom layers.To improve the quality of LDCT sequential images,we proposed a method to remove noise and streak artifacts by using a joint structural dictionary.Firstly,according to the gray-scale features of lung CT image,we divided high resolution CT (HRCT) images into four groups,and obtained four dictionaries for each group by dictionary training.Secondly,computed the information entropy and the histogram of oriented gradient (HOG) of each dictionary atom.And then obtained the structure dictionary of corresponding group by feature clustering.Finally,the joint structural dictionary can be obtained by combining these four structure dictionaries.On the basis of non-local mean filtering for LDCT images,the joint structural dictionary is used as a global dictionary to get the denoised image by sparse representation and reconstruction.In order to validate the effectiveness of the proposed method,simulated and clinical LDCT data are used.Compared with reported methods (KSVD,AS-LNLM and BF-MCA),our method performed better in denoising,streak artifact-removing and details-preserving for the whole LDCT scans,especially for the top and bottom layers.The proposed algorithm can significantly improve the quality of lung LDCT images.
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2018, Vol. 46 
