Compressive Sensing of Medical Images Based on HSV Color Space
Abstract
:1. Introduction
- Proposing a new CS framework by considering HSV color space.
- Proposing a CS approach by exploiting SS Fourier sampling in the measurement approach.
- Proposing a CS reconstruction with HSV loops by exploiting SA, BPDN, and the RA enhancement for MRI, WCE, colonoscopy, and eye images.
2. Related Methods
3. Overview of CS for Medical Images
4. Proposed Methodology
4.1. HSV
4.2. HSV Loop
4.3. Measurement with Spread Spectrum Fourier Sampling
4.4. SA
4.5. CS Reconstruction
Algorithm 1: HSV-SARA |
Input: Measured signal , Sensing matrix , and norm upper bound Output: Reconstructed signal Generate SA basis Initialization ; whiledo |
5. Experiment Setup
5.1. MRI Images
5.2. WCE Images
5.3. Colonoscopy Images
5.4. Private Eye Images
5.5. CS Quality Metrics
6. Experiment Results
6.1. SNR Results
- Regarding the MRI images, RGB–SARA, RGB–BPSA, and RGB–TV were improved by HSV–SARA, with improvements of 2 dB, 6 dB, and 8 dB, respectively.
- Regarding the WCE images, HSV–HSV outperformed RGB–SARA, RGB–BPSA, and RGB–TV, with 2 dB, 10 dB, and 11 dB, respectively.
- Regarding the colonoscopy images, HSV–HSV outperformed RGB–SARA, RGB–BPSA, and RGB–TV, with 2 dB, 5 dB, and 7 dB, respectively.
- Regarding the eye images, HSV–HSV outperformed RGB–SARA, RGB–BPSA, and RGB–TV, with 2 dB, 6 dB, and 7 dB, respectively.
6.2. SSIM Results
- Regarding the MRI images, RGB–SARA, RGB–BPSA, and RGB–TV were improved by HSV–SARA, with improvements of 0.0044, 0.0398, and 0.0238, respectively.
- Regarding the WCE images, HSV–HSV outperformed RGB–SARA, RGB–BPSA, and RGB–TV, with 0.0234, 0.017, and 0.0428, respectively.
- Regarding the colonoscopy images, HSV–HSV outperformed RGB–SARA, RGB–BPSA, and RGB–TV, with 0.0114, 0.0370, and 0.0290, respectively.
- Regarding the eye images, HSV–HSV outperformed RGB–SARA, RGB–BPSA, and RGB–TV, with 0.0068, 0.0216, and 0.0232, respectively.
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Ref | Method Name | Sparsity | Reconstruction | Medical Modality | Color |
---|---|---|---|---|---|
[44] | SARA | SA | BPDN with RA | MRI | Grayscale |
[23] | M-BRA | Multi-SA | BPDN with RA | MRI, CT, WCE, Colonoscopy | Grayscale |
[45] | TV-SARA | SA | TVDN with RA | CT | Grayscale |
[46] | RGB-SARA | SA | BPDN with RA | WCE | RGB |
[47] | RGB-BPSA | SA | BPDN | Eye | RGB |
[48] | RGB-TV | - | TVDN | Eye | RGB |
This article | HSV-SARA | SA | BPDN with RA | MRI, WCE, Colonoscopy, Eye | HSV |
H | S | V | R | G | B | |
---|---|---|---|---|---|---|
Min | 0 | 0 | 0 | 0 | 0 | 0 |
Max | 360 | 100 | 100 | 255 | 255 | 255 |
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Satrya, G.B.; Ramatryana, I.N.A.; Shin, S.Y. Compressive Sensing of Medical Images Based on HSV Color Space. Sensors 2023, 23, 2616. https://doi.org/10.3390/s23052616
Satrya GB, Ramatryana INA, Shin SY. Compressive Sensing of Medical Images Based on HSV Color Space. Sensors. 2023; 23(5):2616. https://doi.org/10.3390/s23052616
Chicago/Turabian StyleSatrya, Gandeva Bayu, I Nyoman Apraz Ramatryana, and Soo Young Shin. 2023. "Compressive Sensing of Medical Images Based on HSV Color Space" Sensors 23, no. 5: 2616. https://doi.org/10.3390/s23052616
APA StyleSatrya, G. B., Ramatryana, I. N. A., & Shin, S. Y. (2023). Compressive Sensing of Medical Images Based on HSV Color Space. Sensors, 23(5), 2616. https://doi.org/10.3390/s23052616