Image smoothing using regularized entropy minimization and self-similarity for the quantitative analysis of drug diffusion
J Cancer Res Ther
;
2020 Sep; 16(5): 1171-1176
Article
| IMSEAR
| ID: sea-213774
ABSTRACT
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 andMethods:
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 andConclusions:
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:
Available
Index:
IMSEAR (South-East Asia)
Journal:
J Cancer Res Ther
Journal subject:
Neoplasms
/
Therapeutics
Year:
2020
Type:
Article
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