基于先进之字形变换和二维逻辑正弦映射(2DLSM)的区域医学图像加密

Prabhavathi K , Anandaraju M B , Vinayakumar Ravi
{"title":"基于先进之字形变换和二维逻辑正弦映射(2DLSM)的区域医学图像加密","authors":"Prabhavathi K ,&nbsp;Anandaraju M B ,&nbsp;Vinayakumar Ravi","doi":"10.1016/j.ijcce.2023.10.001","DOIUrl":null,"url":null,"abstract":"<div><p>A large number of medical images are generated for diagnostic purposes, disease monitoring, research and education, quality control in health services, and so on. The secure transmission and storage of them demand a significant effort. Most of the available encryption schemes are designed for non-medical images, whereas medical images need a higher level of security and robust authentication. Additionally, in certain cases, only a specific part of the image, which may be separated into the region of interest and the region of background, medical images can be divided into these two regions. A region-based medical image encryption using a 2D logistic sine map (2DLSM) and an advanced zig zag transform is used to secure medical images. First, the Region of Interest (ROI) is extracted from the original medical image using basic morphological techniques, including edge detection, dilation, and erosion. Secondly, the ROI is encrypted using a complex zigzag transform and a 2D logistic sine map (2DLSM). Advanced zigzag transform that crosses in both directions while beginning at random points to jumble the image. This new zigzag transform method is more complex than existing zigzag transform techniques because the number of sequence types is equal to the number of pixels in the plaintext image. The confused image is diffused using a random sequence created using the 2D logistic sine map approach after numerous iterations of an advanced zigzag transformation. In order to save time and computational resources, the background region pixels are eliminated during encryption. Experiments and security analyses show that the suggested approach is strong in defending against diverse assaults and can effectively secure ROI of different types and sizes of medical photos.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"4 ","pages":"Pages 349-362"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Region based medical image encryption using advanced zigzag transform and 2D logistic sine map (2DLSM)\",\"authors\":\"Prabhavathi K ,&nbsp;Anandaraju M B ,&nbsp;Vinayakumar Ravi\",\"doi\":\"10.1016/j.ijcce.2023.10.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A large number of medical images are generated for diagnostic purposes, disease monitoring, research and education, quality control in health services, and so on. The secure transmission and storage of them demand a significant effort. Most of the available encryption schemes are designed for non-medical images, whereas medical images need a higher level of security and robust authentication. Additionally, in certain cases, only a specific part of the image, which may be separated into the region of interest and the region of background, medical images can be divided into these two regions. A region-based medical image encryption using a 2D logistic sine map (2DLSM) and an advanced zig zag transform is used to secure medical images. First, the Region of Interest (ROI) is extracted from the original medical image using basic morphological techniques, including edge detection, dilation, and erosion. Secondly, the ROI is encrypted using a complex zigzag transform and a 2D logistic sine map (2DLSM). Advanced zigzag transform that crosses in both directions while beginning at random points to jumble the image. This new zigzag transform method is more complex than existing zigzag transform techniques because the number of sequence types is equal to the number of pixels in the plaintext image. The confused image is diffused using a random sequence created using the 2D logistic sine map approach after numerous iterations of an advanced zigzag transformation. In order to save time and computational resources, the background region pixels are eliminated during encryption. Experiments and security analyses show that the suggested approach is strong in defending against diverse assaults and can effectively secure ROI of different types and sizes of medical photos.</p></div>\",\"PeriodicalId\":100694,\"journal\":{\"name\":\"International Journal of Cognitive Computing in Engineering\",\"volume\":\"4 \",\"pages\":\"Pages 349-362\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Cognitive Computing in Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666307423000335\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Cognitive Computing in Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666307423000335","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

生成大量医学图像用于诊断、疾病监测、研究和教育、卫生服务质量控制等目的。这些图像的安全传输和存储需要付出巨大努力。大多数可用的加密方案都是为非医学图像设计的,而医学图像需要更高级别的安全性和稳健的身份验证。此外,在某些情况下,只有图像的特定部分可以被划分为感兴趣区域和背景区域,医学图像才能被划分为这两个区域。使用2D逻辑正弦映射(2DLSM)和高级Z字形变换的基于区域的医学图像加密用于保护医学图像。首先,使用基本的形态学技术,包括边缘检测、扩张和侵蚀,从原始医学图像中提取感兴趣区域(ROI)。其次,使用复数之字形变换和2D逻辑正弦图(2DLSM)对ROI进行加密。高级Z字形变换,在两个方向上交叉,同时从随机点开始,使图像混乱。这种新的锯齿变换方法比现有的锯齿变换技术更复杂,因为序列类型的数量等于明文图像中的像素数量。在高级Z字形变换的多次迭代之后,使用使用2D逻辑正弦映射方法创建的随机序列来扩散混淆的图像。为了节省时间和计算资源,在加密过程中消除了背景区域像素。实验和安全分析表明,该方法在抵御各种攻击方面具有很强的防御能力,可以有效地保护不同类型和大小的医学照片的ROI。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Region based medical image encryption using advanced zigzag transform and 2D logistic sine map (2DLSM)

A large number of medical images are generated for diagnostic purposes, disease monitoring, research and education, quality control in health services, and so on. The secure transmission and storage of them demand a significant effort. Most of the available encryption schemes are designed for non-medical images, whereas medical images need a higher level of security and robust authentication. Additionally, in certain cases, only a specific part of the image, which may be separated into the region of interest and the region of background, medical images can be divided into these two regions. A region-based medical image encryption using a 2D logistic sine map (2DLSM) and an advanced zig zag transform is used to secure medical images. First, the Region of Interest (ROI) is extracted from the original medical image using basic morphological techniques, including edge detection, dilation, and erosion. Secondly, the ROI is encrypted using a complex zigzag transform and a 2D logistic sine map (2DLSM). Advanced zigzag transform that crosses in both directions while beginning at random points to jumble the image. This new zigzag transform method is more complex than existing zigzag transform techniques because the number of sequence types is equal to the number of pixels in the plaintext image. The confused image is diffused using a random sequence created using the 2D logistic sine map approach after numerous iterations of an advanced zigzag transformation. In order to save time and computational resources, the background region pixels are eliminated during encryption. Experiments and security analyses show that the suggested approach is strong in defending against diverse assaults and can effectively secure ROI of different types and sizes of medical photos.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
13.80
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信