Compressed medical imaging based on average sparsity model and reweighted analysis of multiple basis pursuit

• Published in: Computerized medical imaging and graphics 2021-06, Vol.90, p.101927-101927, Article 101927
• Main Authors: Rahim, Tariq, Novamizanti, Ledya, Apraz Ramatryana, I. Nyoman, Shin, Soo Young
• Format: Article
• Language: English
• Subjects: Algorithms; Average sparsity model; Colon; Colonoscopy; Compressed sensing; Compression; Computed tomography; Data retrieval; Endoscopy; Image processing; Image quality; Image reconstruction; Magnetic resonance imaging; Medical imaging; Multiple basis; Optimization; Reweighted analysis; Sampling; Signal to noise ratio; Sparsity
• ISSN: 0895-6111; 1879-0771
• DOI: 10.1016/j.compmedimag.2021.101927

•Propose compressed medical imaging for a different type of medical images, based on the combination of the average sparsity model and reweighted analysis of multiple basis pursuit (M-BP) reconstruction methods, referred to as multiple basis reweighted analysis (M-BRA).•The proposed algorithm includes the joint multiple sparsity averaging to improves the signal sparsity in M-BP. In this study, four types of medical images are opted to fill the gap of lacking a detailed analysis of M-BRA in medical images. The medical dataset consists of MRI imaging data, CT data, colonoscopy data, and endoscopy data.•Employing the proposed approach, a SNR of 30 dB was achieved for MRI data on a sampling ratio of M/N=0.3. The Signal-to-noise ratio of 34, 30, and 34 dB are corresponding to CT, colonoscopy, and endoscopy data on the same sampling ratio of M/N=0.15. The proposed M-BRA performance indicates the potential for compressed medical imaging analysis with high reconstruction image quality. In medical imaging and applications, efficient image sampling and transfer are some of the key fields of research. The compressed sensing (CS) theory has shown that such compression can be performed during the data retrieval process and that the uncompressed image can be retrieved using a computationally flexible optimization method. The objective of this study is to propose compressed medical imaging for a different type of medical images, based on the combination of the average sparsity model and reweighted analysis of multiple basis pursuit (M-BP) reconstruction methods, referred to as multiple basis reweighted analysis (M-BRA). The proposed algorithm includes the joint multiple sparsity averaging to improves the signal sparsity in M-BP. In this study, four types of medical images are opted to fill the gap of lacking a detailed analysis of M-BRA in medical images. The medical dataset consists of magnetic resonance imaging (MRI) data, computed tomography (CT) data, colonoscopy data, and endoscopy data. Employing the proposed approach, a signal-to-noise ratio (SNR) of 30 dB was achieved for MRI data on a sampling ratio of M/N=0.3. SNR of 34, 30, and 34 dB are corresponding to CT, colonoscopy, and endoscopy data on the same sampling ratio of M/N=0.15. The proposed M-BRA performance indicates the potential for compressed medical imaging analysis with high reconstruction image quality.