期刊
IEEE ACCESS
卷 9, 期 -, 页码 147091-147101出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3124239
关键词
Magnetic resonance imaging; Image reconstruction; Image coding; Signal to noise ratio; Medical diagnostic imaging; Image color analysis; Computed tomography; Compressed imaging; RGB-based; reweighted analysis; sparsity averaging; wireless capsule endoscopy
资金
- Directorate of Research and Community Service, Telkom University
The RGB-SARA method proposed for compressed medical imaging shows higher efficiency and visual quality in medical image processing compared to traditional methods, indicating its potential as a new compression method with high visual quality for medical images.
Compressed medical imaging (CMI) is a medical image sampling process with several samples lower than the Nyquist-Shannon sampling theorem for efficient image sampling; therefore, speeds up the processing time of medical applications. In comparison to previous approaches focusing on single-layer images analysis, this paper proposes CMI using RGB-based sparsity averaging with reweighted analysis (RGB-SARA). The proposed RGB-SARA method is based on the spread spectrum (SS) sampling method, sparsity averaging (SA), basis pursuit denoise (BPDN) reconstruction method, and reweighted analysis (RA). The CS-based SS sampling method compresses each sample in the specific RGB layer followed by SA and BPDN with RA as a sparsity basis and to enhance the performance of CMI reconstruction, respectively. A detailed results analysis is presented in terms of signal-to-noise ratio (SNR), average SNR (ASNR), structural similarity index (SSIM), and processing time demonstrating the efficacy of the proposed RGB-SARA over conventional CMI, i.e., Haar, Daubechies 8 (Db8), and curvelet. A successful demonstration is presented proving that the proposed RGB-SARA is a potential of a new compression method for medical images with high visual quality.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据