Compressive Sensing of Medical Images Based on HSV Color Space

Abstract

Recently, compressive sensing (CS) schemes have been studied as a new compression modality that exploits the sensing matrix in the measurement scheme and the reconstruction scheme to recover the compressed signal. In addition, CS is exploited in medical imaging (MI) to support efficient sampling, compression, transmission, and storage of a large amount of MI. Although CS of MI has been extensively investigated, the effect of color space in CS of MI has not yet been studied in the literature. To fulfill these requirements, this article proposes a novel CS of MI based on hue-saturation value (HSV), using spread spectrum Fourier sampling (SSFS) and sparsity averaging with reweighted analysis (SARA). An HSV loop that performs SSFS is proposed to obtain a compressed signal. Next, HSV–SARA is proposed to reconstruct MI from the compressed signal. A set of color MIs is investigated, such as colonoscopy, magnetic resonance imaging of the brain and eye, and wireless capsule endoscopy images. Experiments were performed to show the superiority of HSV–SARA over benchmark methods in terms of signal-to-noise ratio (SNR), structural similarity (SSIM) index, and measurement rate (MR). The experiments showed that a color MI, with a resolution of 256×256 pixels, could be compressed by the proposed CS at MR of 0.1, and could be improved in terms of SNR being 15.17% and SSIM being 2.53%. The proposed HSV–SARA can be a solution for color medical image compression and sampling to improve the image acquisition of medical devices.

Red/Orange Autofluorescence in Selected Candida Strains Exposed to 405 nm Laser Light

“…This model finds wide applications across various scientific and technological domains, including medical research. For instance, it has been employed in the analysis of kidney histology images [ 29 ], in the study of patients undergoing treatment for ischemic heart disease to assess tongue appearance [ 30 ], and as a foundational approach for comprehensive image analysis in medicine [ 31 ].…”

“…Of the existing color systems, including RGB (Red, Green, Blue), HSV/HSB (Hue, Saturation, Value / Hue, Saturation, Brightness), and CIELAB (International Commission on Illumination), the HSV/HSB model was selected for our analysis. The HSV color space is extensively applied in image retrieval contexts, aligns with human visual sensation, and enables the robust characterization of image content [ 28 , 29 , 30 , 31 ].…”