Two-Bit Embedding Histogram-Prediction-Error Based Reversible Data Hiding for Medical Images with Smooth Area

During medical treatment, personal privacy is involved and must be protected. Healthcare institutions have to keep medical images or health information secret unless they have permission from the data owner to disclose them. Reversible data hiding (RDH) is a technique that embeds metadata into an im...

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Autores principales:	Ching-Yu Yang, Ja-Ling Wu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	data hiding reversible data hiding histogram shifting prediction error medical image ROI Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/88db299a87604c45969e29256df7b00d
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id	oai:doaj.org-article:88db299a87604c45969e29256df7b00d
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spelling	oai:doaj.org-article:88db299a87604c45969e29256df7b00d2021-11-25T17:17:30ZTwo-Bit Embedding Histogram-Prediction-Error Based Reversible Data Hiding for Medical Images with Smooth Area10.3390/computers101101522073-431Xhttps://doaj.org/article/88db299a87604c45969e29256df7b00d2021-11-01T00:00:00Zhttps://www.mdpi.com/2073-431X/10/11/152https://doaj.org/toc/2073-431XDuring medical treatment, personal privacy is involved and must be protected. Healthcare institutions have to keep medical images or health information secret unless they have permission from the data owner to disclose them. Reversible data hiding (RDH) is a technique that embeds metadata into an image and can be recovered without any distortion after the hidden data have been extracted. This work aims to develop a fully reversible two-bit embedding RDH algorithm with a large hiding capacity for medical images. Medical images can be partitioned into regions of interest (ROI) and regions of noninterest (RONI). ROI is informative with semantic meanings essential for clinical applications and diagnosis and cannot tolerate subtle changes. Therefore, we utilize histogram shifting and prediction error to embed metadata into RONI. In addition, our embedding algorithm minimizes the side effect to ROI as much as possible. To verify the effectiveness of the proposed approach, we benchmarked three types of medical images in DICOM format, namely X-ray photography (X-ray), computer tomography (CT), and magnetic resonance imaging (MRI). Experimental results show that most of the hidden data have been embedded in RONI, and the performance achieves high capacity and leaves less visible distortion to ROIs.Ching-Yu YangJa-Ling WuMDPI AGarticledata hidingreversible data hidinghistogram shiftingprediction errormedical imageROIElectronic computers. Computer scienceQA75.5-76.95ENComputers, Vol 10, Iss 152, p 152 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	data hiding reversible data hiding histogram shifting prediction error medical image ROI Electronic computers. Computer science QA75.5-76.95
spellingShingle	data hiding reversible data hiding histogram shifting prediction error medical image ROI Electronic computers. Computer science QA75.5-76.95 Ching-Yu Yang Ja-Ling Wu Two-Bit Embedding Histogram-Prediction-Error Based Reversible Data Hiding for Medical Images with Smooth Area
description	During medical treatment, personal privacy is involved and must be protected. Healthcare institutions have to keep medical images or health information secret unless they have permission from the data owner to disclose them. Reversible data hiding (RDH) is a technique that embeds metadata into an image and can be recovered without any distortion after the hidden data have been extracted. This work aims to develop a fully reversible two-bit embedding RDH algorithm with a large hiding capacity for medical images. Medical images can be partitioned into regions of interest (ROI) and regions of noninterest (RONI). ROI is informative with semantic meanings essential for clinical applications and diagnosis and cannot tolerate subtle changes. Therefore, we utilize histogram shifting and prediction error to embed metadata into RONI. In addition, our embedding algorithm minimizes the side effect to ROI as much as possible. To verify the effectiveness of the proposed approach, we benchmarked three types of medical images in DICOM format, namely X-ray photography (X-ray), computer tomography (CT), and magnetic resonance imaging (MRI). Experimental results show that most of the hidden data have been embedded in RONI, and the performance achieves high capacity and leaves less visible distortion to ROIs.
format	article
author	Ching-Yu Yang Ja-Ling Wu
author_facet	Ching-Yu Yang Ja-Ling Wu
author_sort	Ching-Yu Yang
title	Two-Bit Embedding Histogram-Prediction-Error Based Reversible Data Hiding for Medical Images with Smooth Area
title_short	Two-Bit Embedding Histogram-Prediction-Error Based Reversible Data Hiding for Medical Images with Smooth Area
title_full	Two-Bit Embedding Histogram-Prediction-Error Based Reversible Data Hiding for Medical Images with Smooth Area
title_fullStr	Two-Bit Embedding Histogram-Prediction-Error Based Reversible Data Hiding for Medical Images with Smooth Area
title_full_unstemmed	Two-Bit Embedding Histogram-Prediction-Error Based Reversible Data Hiding for Medical Images with Smooth Area
title_sort	two-bit embedding histogram-prediction-error based reversible data hiding for medical images with smooth area
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/88db299a87604c45969e29256df7b00d
work_keys_str_mv	AT chingyuyang twobitembeddinghistogrampredictionerrorbasedreversibledatahidingformedicalimageswithsmootharea AT jalingwu twobitembeddinghistogrampredictionerrorbasedreversibledatahidingformedicalimageswithsmootharea
_version_	1718412542498308096

Two-Bit Embedding Histogram-Prediction-Error Based Reversible Data Hiding for Medical Images with Smooth Area

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