Enhancing Security in Medical Image Informatics with Various Attacks

A. Umamageswari, A. Jebasheela, Ruby Durairaj, Dr. M. A. Leo Vijilious
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Abstract

The objective of the work is to provide security to the medical images by embedding medical data (EPR-Electronic Patient Record) along with the image to reduce the bandwidth during communication. Reversible watermarking and Digital Signature itself will provide high security. This application mainly used in tele-surgery (Medical Expert to Medical Expert Communication). Only the authorized medical experts can explore the patients’ image because of Kerberos. The proposed work is mainly to restrict the unauthorized access to get the patients'data. So medical image authentication may be achieved without biometric recognition such as finger prints and eye stamps etc. The EPR itself contains the patients’ entire history, so after the extraction process Medical expert can able to identify the patient and also the disease information. In future we can embed the EPR inside the medical image after it got encrypted to achieve more security. To increase the authentication, Medical Expert biometric information can be embedded inside the image in the future. Experiments were conducted using more than 500 (512×512) image archives in various modalities from the NIH (National Institute of Health) and Aycan sample digital images downloaded from the internet and tests are conducted. Almost in all images with greater than 15000 bits embedding size and got PSNR of 60.4 dB to 78.9 dB with low distortion in received image because of compression, not because of watermarking and average NPCR (Number of Pixels Change Rate) is 98.9 %.
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针对各种攻击增强医学图像信息的安全性
该工作的目的是通过在图像中嵌入医疗数据(epr -电子病历)来保证医学图像的安全性,以减少通信过程中的带宽。可逆水印和数字签名本身将提供高安全性。该应用程序主要用于远程手术(医学专家对医学专家通信)。由于Kerberos,只有经过授权的医学专家才能对患者的图像进行探索。所提出的工作主要是限制未经授权的访问以获取患者数据。因此,医学图像认证可以在不需要指纹、眼戳等生物特征识别的情况下实现。EPR本身包含了患者的整个病史,因此在提取过程后,医学专家可以识别患者和疾病信息。在未来,我们可以将EPR嵌入经过加密的医学图像中,以达到更高的安全性。为了增加身份验证,未来可以将医学专家的生物特征信息嵌入到图像中。实验使用了来自美国国立卫生研究院(NIH)和Aycan从互联网下载的数字图像样本的500多张不同形式的图像档案(512×512)进行,并进行了测试。在几乎所有大于15000位嵌入尺寸的图像中,通过压缩而非水印,接收图像的PSNR在60.4 dB ~ 78.9 dB之间,失真程度低,平均NPCR (Number of Pixels Change Rate)为98.9%。
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