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| Title | Compressed Sensing Reconstruction Algorithms For Medical Images – A Comparison |
|---|---|
| ID_Doc | 15346 |
| Authors | Kavitha K.J.; Manur V.B.; Suprith P.G.; Naik M.S.; Chaitra S.N. |
| Year | 2024 |
| Published | Smart Hospitals: 5G, 6G and Moving Beyond Connectivity |
| DOI | http://dx.doi.org/10.1002/9781394275472.ch4 |
| Abstract | The Internet of Things (IoT) is a rapidly developing field of technology that has the potential to revolutionize healthcare, smart cities, daily human activities, manufacturing and other industries. Incorporating compressive sensing (CS) into the design of IoT platforms is a highly appealing paradigm. The medical imaging community has been very interested in CS because of its potential to produce high-quality picture reconstructions with less data by taking use of compressibility. In this paper, we have discussed various compressing reconstruction algorithms that could be used in the medical imaging application for better image construction by eliminating unnecessary redundant bits. The orthogonal matching pursuit algorithm (OMP), sparsity-adaptive matching pursuit algorithm (SaMP), adaptive step-size SaMP algorithm (AS-SaMP) and dynamic-step-size SaMP (DSS-SaMP) are discussed, evaluated and compared with each other in terms of bit error rate (BER), signal error rate (SER), and mean square error (MSE). © 2024 Scrivener Publishing LLC. |
| Author Keywords | AS-SaMP; BER; CS; DSS-SaMP; IoT; MSE; OMP; SaMP; SER |
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