Image smoothing using regularized entropy minimization and self-similarity for the quantitative analysis of drug diffusion
| Autor: | Lu Wang, Weifeng Zhou, Shibing Xiang, Yixiao Li, Hongbin Han, Shenghai Liao, Bin Liu, Shujun Fu, Xiangbin Meng, Yuliang Li |
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| Rok vydání: | 2020 |
| Předmět: |
Self-similarity
Computer science Entropy Liver Neoplasms Antineoplastic Agents General Medicine Real image Magnetic Resonance Imaging Imaging analysis Diffusion Binary entropy function Drug Delivery Systems Oncology Delayed-Action Preparations Prior probability Image Processing Computer-Assisted Humans Entropy (information theory) Radiology Nuclear Medicine and imaging Algorithm Algorithms Smoothing Entropy minimization |
| Zdroj: | Journal of Cancer Research and Therapeutics. 16:1171 |
| ISSN: | 0973-1482 |
| DOI: | 10.4103/jcrt.jcrt_656_20 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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