基于新型加密压缩方案的医学图像传输

IF 2 4区 计算机科学 Q2 Computer Science Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI:10.32604/iasc.2022.021636
Arwa A. Mashat, Surbhi Bhatia, Ankit Kumar, P. Dadheech, A. Alabdali
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引用次数: 3

摘要

医疗记录在不连贯和开放的网络上传输,由于这些链接缺乏安全性,导致涉及窃取患者信息的欺诈行为增加。个人的医疗文件是机密信息,需要严格的协议和安全,主要是为了保护个人的身份。医学图像保护是一种旨在通过公共网络安全地传输数字数据和医学图像的技术。本文介绍了医疗信息安全中用于提供身份验证和保护的不同方法的背景。本工作开发了一种安全的基于密码学的医学图像复原算法,该算法基于多种技术:离散余弦变换、隐写和水印。该算法以图像的形式获取患者信息,利用人工智能和水印相结合的离散余弦变换方法计算图像的峰值信噪比值。该框架使用底层算法对图像进行加密和解密,同时保持峰值信噪比值。该值使用置乱算法隐藏;因此,访问真实图像需要一个唯一的患者密码。所提出的技术被证明是鲁棒的,因此能够防止数据被窃取。给出了仿真实验的结果,并与各种已验证的算法进行了比较,证明了新方法的准确性。
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Medical Image Transmission Using Novel Crypto-Compression Scheme
The transmission of medical records over indiscrete and open networks has caused an increase in fraud involving stealing patients’ information, owing to a lack of security over these links. An individual’s medical documents represent confidential information that demands strict protocols and security, chiefly to protect the individual’s identity. Medical image protection is a technology intended to transmit digital data and medical images securely over public networks. This paper presents some background on the different methods used to provide authentication and protection in medical information security. This work develops a secure cryptography-based medical image reclamation algorithm based on a combination of techniques: discrete cosine transform, steganography, and watermarking. The novel algorithm takes patients’ information in the form of images and uses a discrete cosine transform method with artificial intelligence and watermarking to calculate peak signal-to-noise ratio values for the images. The proposed framework uses the underlying algorithms to perform encryption and decryption of images while retaining a high peak signal-to-noise ratio value. This value is hidden using a scrambling algorithm; therefore, a unique patient password is required to access the real image. The proposed technique is demonstrated to be robust and thus able to prevent stealing of data. The results of simulation experiments are presented, and the accuracy of the new method is demonstrated by comparisons with various previously validated algorithms.
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来源期刊
Intelligent Automation and Soft Computing
Intelligent Automation and Soft Computing 工程技术-计算机:人工智能
CiteScore
3.50
自引率
10.00%
发文量
429
审稿时长
10.8 months
期刊介绍: An International Journal seeks to provide a common forum for the dissemination of accurate results about the world of intelligent automation, artificial intelligence, computer science, control, intelligent data science, modeling and systems engineering. It is intended that the articles published in the journal will encompass both the short and the long term effects of soft computing and other related fields such as robotics, control, computer, vision, speech recognition, pattern recognition, data mining, big data, data analytics, machine intelligence, cyber security and deep learning. It further hopes it will address the existing and emerging relationships between automation, systems engineering, system of systems engineering and soft computing. The journal will publish original and survey papers on artificial intelligence, intelligent automation and computer engineering with an emphasis on current and potential applications of soft computing. It will have a broad interest in all engineering disciplines, computer science, and related technological fields such as medicine, biology operations research, technology management, agriculture and information technology.
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