Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 
ORIGINAL ARTICLE
Year : 2020  |  Volume : 16  |  Issue : 5  |  Page : 1171-1176

Image smoothing using regularized entropy minimization and self-similarity for the quantitative analysis of drug diffusion


1 Physical Examination Office, Health Commission of Shandong Province, Jinan, Shandong, China
2 Department of Infrastructure Management, Qilu Hospital of Shandong University, Jinan, Shandong, China
3 Department of Intervention Medicine, The Second Hospital of Shandong University, Jinan, Shandong, China
4 School of Mathematics, Shandong University, Jinan, Shandong, China
5 School of Mathematics and Physics, Qingdao University of Science and Technology, Shandong, China
6 College of Basic Medicine, Jining Medical University, Shenghua, China
7 Department of Radiology, Peking University Third Hospital; Beijing Key Laboratory of Magnetic Resonance Imaging Equipment and Technique, Beijing, China

Correspondence Address:
Shujun Fu
School of Mathematics, Shandong University, Jinan
China
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jcrt.JCRT_656_20

Rights and Permissions

Background: Targetable drug delivery is an important method for the treatment of liver tumors. For the quantitative analysis of drug diffusion, the establishment of a method for information collection and characterization of extracellular space is developed by imaging analysis of magnetic resonance imaging (MRI) sequences. In this paper, we smoothed out interferential part in scanned digital MRI images. Materials and Methods: Making full use of priors of low rank, nonlocal self-similarity, and regularized sparsity-promoting entropy, a block-matching regularized entropy minimization algorithm is proposed. Sparsity-promoting entropy function produces much sparser representation of grouped nonlocal similar blocks of image by solving a nonconvex minimization problem. Moreover, an alternating direction method of multipliers algorithm is proposed to iteratively solve the problem above. Results and Conclusions: Experiments on simulated and real images reveal that the proposed method obtains better image restorations compared with some state-of-the-art methods, where most information is recovered and few artifacts are produced.


[FULL TEXT] [PDF]*
Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)
 

 Article Access Statistics
    Viewed187    
    Printed0    
    Emailed0    
    PDF Downloaded5    
    Comments [Add]    

Recommend this journal