RGB-Based Compressed Medical Imaging Using Sparsity Averaging Reweighted Analysis for Wireless Capsule Endoscopy 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 imag...

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Autores principales:	Rita Magdalena, Tariq Rahim, I Putu Agus Eka Pratama, Ledya Novamizanti, I Nyoman Apraz Ramatryana, Aamir Younas Raja, Soo Young Shin
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Compressed imaging RGB-based reweighted analysis sparsity averaging wireless capsule endoscopy Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/b2827c88c9b44316a742fec22d7a6d6f
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id	oai:doaj.org-article:b2827c88c9b44316a742fec22d7a6d6f
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spelling	oai:doaj.org-article:b2827c88c9b44316a742fec22d7a6d6f2021-11-18T00:05:23ZRGB-Based Compressed Medical Imaging Using Sparsity Averaging Reweighted Analysis for Wireless Capsule Endoscopy Images2169-353610.1109/ACCESS.2021.3124239https://doaj.org/article/b2827c88c9b44316a742fec22d7a6d6f2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9594844/https://doaj.org/toc/2169-3536Compressed 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.Rita MagdalenaTariq RahimI Putu Agus Eka PratamaLedya NovamizantiI Nyoman Apraz RamatryanaAamir Younas RajaSoo Young ShinIEEEarticleCompressed imagingRGB-basedreweighted analysissparsity averagingwireless capsule endoscopyElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 147091-147101 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Compressed imaging RGB-based reweighted analysis sparsity averaging wireless capsule endoscopy Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Compressed imaging RGB-based reweighted analysis sparsity averaging wireless capsule endoscopy Electrical engineering. Electronics. Nuclear engineering TK1-9971 Rita Magdalena Tariq Rahim I Putu Agus Eka Pratama Ledya Novamizanti I Nyoman Apraz Ramatryana Aamir Younas Raja Soo Young Shin RGB-Based Compressed Medical Imaging Using Sparsity Averaging Reweighted Analysis for Wireless Capsule Endoscopy Images
description	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.
format	article
author	Rita Magdalena Tariq Rahim I Putu Agus Eka Pratama Ledya Novamizanti I Nyoman Apraz Ramatryana Aamir Younas Raja Soo Young Shin
author_facet	Rita Magdalena Tariq Rahim I Putu Agus Eka Pratama Ledya Novamizanti I Nyoman Apraz Ramatryana Aamir Younas Raja Soo Young Shin
author_sort	Rita Magdalena
title	RGB-Based Compressed Medical Imaging Using Sparsity Averaging Reweighted Analysis for Wireless Capsule Endoscopy Images
title_short	RGB-Based Compressed Medical Imaging Using Sparsity Averaging Reweighted Analysis for Wireless Capsule Endoscopy Images
title_full	RGB-Based Compressed Medical Imaging Using Sparsity Averaging Reweighted Analysis for Wireless Capsule Endoscopy Images
title_fullStr	RGB-Based Compressed Medical Imaging Using Sparsity Averaging Reweighted Analysis for Wireless Capsule Endoscopy Images
title_full_unstemmed	RGB-Based Compressed Medical Imaging Using Sparsity Averaging Reweighted Analysis for Wireless Capsule Endoscopy Images
title_sort	rgb-based compressed medical imaging using sparsity averaging reweighted analysis for wireless capsule endoscopy images
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/b2827c88c9b44316a742fec22d7a6d6f
work_keys_str_mv	AT ritamagdalena rgbbasedcompressedmedicalimagingusingsparsityaveragingreweightedanalysisforwirelesscapsuleendoscopyimages AT tariqrahim rgbbasedcompressedmedicalimagingusingsparsityaveragingreweightedanalysisforwirelesscapsuleendoscopyimages AT iputuagusekapratama rgbbasedcompressedmedicalimagingusingsparsityaveragingreweightedanalysisforwirelesscapsuleendoscopyimages AT ledyanovamizanti rgbbasedcompressedmedicalimagingusingsparsityaveragingreweightedanalysisforwirelesscapsuleendoscopyimages AT inyomanaprazramatryana rgbbasedcompressedmedicalimagingusingsparsityaveragingreweightedanalysisforwirelesscapsuleendoscopyimages AT aamiryounasraja rgbbasedcompressedmedicalimagingusingsparsityaveragingreweightedanalysisforwirelesscapsuleendoscopyimages AT sooyoungshin rgbbasedcompressedmedicalimagingusingsparsityaveragingreweightedanalysisforwirelesscapsuleendoscopyimages
_version_	1718425245854990336

RGB-Based Compressed Medical Imaging Using Sparsity Averaging Reweighted Analysis for Wireless Capsule Endoscopy Images

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