Embedding information reversibly in medical images for e-health

Author:

Kamili Asra1,Fatima Izat1,Hassan Muzamil1,Parah Shabir A.,Vijaya Kumar V.2,Ambati L. S.3

Affiliation:

1. Department of Electronics and Instrumentation Technology, University of Kashmir, India

2. School of Computer Engineering, Nobel International University, USA

3. Department of Business Information Systems, Dakota State University, USA

Abstract

Embedding information in medical images is considered as one of the significant methods for safeguarding the integrity and authenticity of medical images besides providing security to electronic patient records (EPR). The conventional embedding methods deteriorate the perceptual quality of medical images making them unsuitable for proper diagnosis. To preserve the perceptual quality of medical images reversible embedding is used. The reversible embedding schemes, however, have less embedding capacity. In this work, a reversible scheme based on histogram bin shifting and RGB plane concatenation has been proposed which offers high embedding capacity as well. We have exploited the fact that medical images, unlike general images, consist of a large number of peaks and zero points that can be employed for reversibly embedding the data. Reversibility ensures that original image restoration takes place after the extraction of embedded data, which is of great importance in medical images for proper diagnosis and treatment. We have used various subjective and objective image quality metrics for analyzing the scheme. The proposed scheme has been shown to provide a Peak Signal to Noise Ratio (PSNR) value of above 56 dB for an embedding capacity of 0.58 bits per pixel (bpp). The results obtained show that the performance of scheme presented is far better in comparison to the state-of-the-art.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference18 articles.

Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3