Hybrid watermarking of medical images for ROI authentication and recovery

Medical image data require strict security, confidentiality and integrity. To achieve these stringent requirements, we propose a hybrid watermarking method which embeds a robust watermark in the region of non-interest (RONI) for achieving security and confidentiality, while integrity control is achieved by inserting a fragile watermark into the region of the interest (ROI). First the information to be modified in ROI is separated and is inserted into RONI, which later is used in recovery of the original ROI. Secondly, to avoid the underflow and overflow, a location map is generated for embedding the watermark block-wise by leaving the suspected blocks. This avoids the preprocessing step of histogram modification. The image visual quality, as well as tamper localization, is evaluated. We use weighted peak signal to noise ratio for measuring image quality of watermarked images. Experimental results show that the proposed method outperforms the existing hybrid watermarking techniques.     

一种医学影像数据选择性的加密方法

随着云计算与大数据技术的发展,为解决昂贵医学影像设备所产生的在国内区域影像数据安全共享服务要求,结合多媒体特别是医学影像数据量大等特点,基于S CAN语言提出一种选择性加密算法,结合数据本身的哈希值作为位加密周期序列,实现个人医学影像数据的隐私安全保护。通过安全性分析与仿真实验证明该算法具有较好的置乱等效果。     

数字签名和数字水印在电子病历中的应用

文章在分析电子病历的机密性、完整性、不可抵赖性、认证性、有效性等安全需求的基础上,提出了一种应用于电子病历的,综合运用数字签名和数字水印技术的体系结构,并且讨论了该结构的安全性能。     

基于感兴趣区域的图像水印嵌入算法研究

鉴于现有绝大多数图像水印方案将载体图像看作整个场景,并未考虑载体图像自身的结构特征及人们感兴趣的视觉目标区域,本文提出了一种基于感兴趣区(ROI)的小波域图像水印嵌入算法.该算法首先结合人眼视觉感知特性,在小波变换域内利用k-均值聚类提取出感兴趣区域;然后对感兴趣区域进行小波变换,并结合图像局部相关性对小波系数进行自适应的量化调制,将水印信息嵌入到感兴趣区域的低频子带内.仿真实验表明,所提出的水印嵌入算法不仅具有较好的透明性,而且对JPEG压缩、叠加噪声、锐化等常规图像处理操作具有较好的鲁棒性.特别地,该算法能够有效抵御恶意剪切、目标移动、替换背景等攻击,而且可以实现盲检测.     

Hiding iris data for authentication of digital images using wavelet theory

This paper introduces an efficient approach to protect the ownership by hiding an iris data into digital image for an authentication purpose. It is based on the theory of wavelets. The idea is to secretly embed biometric data (iris print) in the content of the image identifying the owner. The system is based on an empirical analysis of biometric and watermarking technologies, and it is split into several processes. The first process is based on iris image analysis, which aids the generation of the iris code (watermark); the second and the third processes deal with embedding and detecting a watermark; and the last process deals with the authentication. A new metric that measures the objective quality of the image based on the detected watermark bit is introduced, which does not require the original unmarked image for watermark detection. Simulation results show the effectiveness and efficiency of the proposed approach. The text was submitted by the author in English.     

基于视觉注意的医学图像感兴趣区域提取*

提出一种基于视觉注意机制的医学图像感兴趣区域提取方法。受生物学启发,该方法模仿人类自下而上的视觉选择性注意过程,通过计算图像中每个像素的全局对比度,构造相应的显著图,并根据显著图,检测出图像中的显著区域。利用多幅医学图像对本方法进行评估,结果表明,该方法能够快速、精确地提取图像中的感兴趣区域,在提取结果和运算速度上均取得了令人满意的效果。     

面向临床科研的医疗事件模型与开放数据集合构建

基于电子病历观察性数据的真实世界研究成为目前临床科研的热点。然而关系数据模型无法直接支撑起科研应用中医疗事件的时序关系表示以及知识融合的查询需求。针对上述问题,该文提出了一种新的基于RDF的医疗观察性数据表示模型,该模型可以清晰地表示临床检查、诊断、治疗等多种事件类型以及事件的时序关系。对来源于医院的电子病历数据,经过数据预处理、数据模式转换、时序关系构建以及知识融合4个步骤建立事件图谱。具体地,使用三家上海三甲医院的电子病历数据,构建了包括3个专科、173 395个医疗事件以及501 335个事件时序关系的医疗数据集,并融合了5 313个中文医疗知识库概念。基于临床文献与医生科研需求,该文根据公共卫生流行病学的病因研究、治疗研究等类型,分别提供了针对本数据集的40个问题示例,并将其中的部分问题与传统关系数据库在查询的构建与执行方面进行了实验比对,论证了该事件图谱的优越性。该数据集遵循开放链接标准,在OpenKG上发布并提供了在线访问的SPARQL站点,链接为 https://peg.ecustnlplab.com/dataset.html。     

结合顶点趋势检测的三维模型数字水印算法

为了提高三维模型空域数字水印算法对抗各类攻击尤其是简化、剪切及其联合攻击的鲁棒性,提出一种新的结合顶点趋势检测的三维模型数字水印算法。利用三维模型顶点的球面坐标构建有序顶点集,选择体现三维模型整体形状信息的顶点范数嵌入水印;通过比较和统计待检测模型和原始模型顶点范数的变化关系,以变化趋势为依据实现水印检测,并分析顶点缺失率进一步弱化剪切对水印检测的影响。实验表明,该算法对各种常见的单一类型攻击如平移、旋转、均匀缩放、顶点重排序、噪声、简化、剪切、量化和细分以及联合攻击都具有较好的鲁棒性,对平滑攻击也有一定的抵抗能力,能够较好地应用于三维模型的版权保护。     

用于实现医学图像ROI对比度增强的可逆数据隐藏算法

基于可逆数据隐藏技术具有可以准确地提取嵌入的秘密消息并无损还原原始载体的特点,可逆数据隐藏技术被广泛使用在医学和军事等领域。提出一种新的基于直方图平移的可逆数据隐藏算法,针对医学图像的像素分布特点,利用最大类间方差法分离背景和前景区域,确定边缘像素。对已有算法的预处理过程进行改进,使其能够自适应地选择最优预处理区间,降低视觉失真,并在信息嵌入的同时使医学图像感兴趣区域的对比度增强效果得到逐步改善,最终原始医学图像可以无失真地恢复。与现在已知的方法相比,新算法可以使医学图像ROI的对比度增强效果得到更明显的改善,大幅度提高了图像的视觉质量。     

医学图像可逆数字水印防溢出处理研究

针对医学图像数字水印嵌入过程中出现的大量溢出现象,对文献中提及的几种常见防溢出算法--module-256、位置映射和直方图压缩方法进行了对比实验。实验结果给出了三种方法对可逆水印算法的容量和图像质量造成的影响,证明了直方图压缩方法的优越性。分析指出防溢出算法是医学图像可逆数字水印性能的关键,其算法选择取决于具体的可逆水印算法和医学载体图像的不同形态。     

医学图像的关联规则挖掘方法研究

提出了一个基于关联规则挖掘算法的医学图像分析器。介绍了数量型属性离散化的CA算法、关联规则挖掘算法及其在医学图像数据挖掘中的应用方法,并利用介绍的算法对乳腺癌图像数据进行挖掘。实验结果表明,该模型系统达到了较高的分类准确率。     

YOLO-based ROI selection for joint encryption and compression of medical images with reconstruction through super-resolution network

Medical images contain significant patient information, and this confidential data should not be accessed without proper authorisation. Concurrently, due to the high redundancy of image data, compression is necessary to minimise image size and efficiently utilise network resources. This paper presents an effective joint encryption and compression method for medical images that prevent critical data leakage while reducing redundancy. Initially, a powerful real-time object detection method, You Only Look Once v7, is employed to accurately and swiftly detect the region of interest (ROI) within the medical images. Subsequently, a joint three-dimensional chaotic map and Huffman encoding are applied to secure medical images without compromising the compression ratio or increasing the time cost. Lastly, a super-resolution network is established at the receiver end to better reconstruct the ROI image for precise diagnostic purposes. The comprehensive experimental analysis demonstrates that our method delivers high levels of security, compression, and visual quality performance on standard datasets used in smart healthcare applications, at a minimum. Furthermore, our approach outperforms other competitive state-of-the-art schemes when compared. We hope this study will inspire further research within the healthcare community.     

自适应高容量医学图像可逆数据隐藏算法

提出了一种新的医学图像无损数据隐藏算法,根据医学图像特点,将隐秘信息分别嵌入在感兴趣区域和非感兴趣区域中。在非感兴趣区域中,采用自适应整数变换方法增大嵌入容量并控制失真;在感兴趣区域,采用最低有效位替换方法控制水印图像质量。实验结果表明,算法的嵌入容量在1.2bpp到1.7bpp之间,而峰值信噪比保持在43dB左右。与现有相关算法相比,嵌入容量具有明显优势并保持较高的图像质量。另外,本方法计算复杂度低,能方便应用于实际的医学信息系统中。     

An anonymous authentication and secure data transmission scheme for the Internet of Things based on blockchain

With the widespread use of network infrastructures such as 5G and low-power wide-area networks, a large number of the Internet of Things (IoT) device nodes are connected to the network, generating massive amounts of data. Therefore, it is a great challenge to achieve anonymous authentication of IoT nodes and secure data transmission. At present, blockchain technology is widely used in authentication and s data storage due to its decentralization and immutability. Recently, Fan et al. proposed a secure and efficient blockchain-based IoT authentication and data sharing scheme. We studied it as one of the state-of-the-art protocols and found that this scheme does not consider the resistance to ephemeral secret compromise attacks and the anonymity of IoT nodes. To overcome these security flaws, this paper proposes an enhanced authentication and data transmission scheme, which is verified by formal security proofs and informal security analysis. Furthermore, Scyther is applied to prove the security of the proposed scheme. Moreover, it is demonstrated that the proposed scheme achieves better performance in terms of communication and computational cost compared to other related schemes.     

采用旋转校正的指静脉图像感兴趣区域提取方法

针对静脉图像采样过程中存在的旋转、平移等非线性因素造成手指静脉图像定位困难的问题,考虑图像非接触式采集特点,提出一种采用旋转校正的手指静脉图像感兴趣区域提取方法.首先对读入的手指静脉图像采用Kapur熵阈值法分割出手指区域,再依据图像的质心对图像进行旋转校正,最后根据图像中每列像素竖直方向上的投影值和手指区域的边缘轮廓,确定出感兴趣区域的位置.实验结果表明,该方法能够准确地提取出静脉图像的感兴趣区域,有效地提高识别系统的性能.     

A low-cost method for reliable ownership identification of medical images using SVM and Lagrange duality

Prevention of false positive and false negative errors is a major challenge for ownership identification and proof of ownership applications using digital image watermarking. Such errors are more critical with sensitive data, such as electronic patient records (EPRs) in medical image watermarking. A false positive error is a watermark detection error, which means that a watermark is detected in a media where there is no watermark. In contrast, a false negative error is an inability of the watermark detector to detect an embedded watermark in a watermarked image. These errors make ownership assessments unreliable, and the incorrect ownership identification of a patient’s record could result in failure of the correct diagnostics and treatments. To address this type of problem, a low-cost technique based on a support vector machine (SVM) and Lagrange duality was proposed to achieve reliable approximations for ownership identification in medical image watermarking without requiring the correction of attacked watermarked images. In this technique, the results of the ownership evaluation are categorized into two independent classes, namely watermark-detected and watermark-not-detected, and higher geometric margins between these classes are associated with higher reliability. To address additional situations with false positive and false negative errors, four different situations, including watermarked, unwatermarked, attacked watermarked and attacked unwatermarked images, were investigated. Experiments were conducted on duo-ISB-bit-plane (BiISB) watermarking using the histogram intersection (HI) technique as a testing platform under JPEG2000 and JPEG image compression attacks and using two groups of images: standard image processing images and X-ray medical images. The experimental investigations revealed that the HI technique guarantees that the rightful owner can be reliably identified even after severe attacks and in the face of context similarities between the watermark and the embedding pixels of the host image.     

脑血肿CT图像分割的改进快速行进方法

为解决脑血肿CT图像的手动分割和半自动分割方法在手术计划制定过程中存在分割速度无法满足临床时间要求的问题,对快速行进(fast marching,FM)方法进行改进,提出一种三维全自动脑血肿CT图像分割方法。利用阈值处理自动获取种子点集,解决手动设置种子点的问题,实现全自动;提取感兴趣区域减少计算量;通过感兴趣区域金字塔的迭代分割进一步减少FM方法的演化计算量,使分割结果快速向边界收敛。实验结果表明,该方法能够自动、准确并高效地分割脑血肿CT图像。     

根系CT序列图像区域生长分割的新方法

在对传统区域生长算法改进的基础上,针对原位根系CT序列图像的特点,提出了一种基于区域生长的植物根系CT序列图像分割算法。通过对208幅JPEG格式的植物根系CT序列图像进行直方图分析,确定植物根系区域的分割阈值范围,结合阈值分割实现改进区域生长法对单层根系图像进行分割得到目标区域。在此基础上,利用植物根系在介质环境中的空间连续性,进一步实现仅在选择单幅图像种子点的情况下一次性完成整套CT序列图像的分割。借助MITK（Medical Imaging Toolkit）工具箱对分割好的原位根系CT序列图像进行三维重建,对三维模型进行不同角度观测来判断分割的正确性。实验结果表明,该算法分割速度快、精度高,能够有效地去除CT图像背景中杂质像素,准确提取出植物根系目标区域。     

基于经纬网格的递增KNN位置隐私保护查询算法

为了在使用基于位置的服务时用户的位置信息不被不可信的位置服务提供商所泄漏,k-匿名位置隐私保护已被广泛研究.然而在集中匿名器被黑客控制时原k-匿名算法会泄漏所有用户的位置隐私,在进行k个最近邻目标查询时对网络的负载较重,而SpaceTwist算法又不能保证k-匿名.提出了一种基于经纬网格的递增KNN位置隐私保护查询算法,将经典的k-匿名算法与SpaceTwist算法相杂交,并引入经纬网格代替原来精确的位置上报给集中匿名器,从而解决了上述问题.实验证明基于经纬网格的递增KNN查询算法比较节省从集中匿名器到位置服务提供商的服务器之间的网络流量.     

基于医学征象和卷积神经网络的肺结节CT图像哈希检索

针对肺结节图像检索中存在的两个问题:手工设计的特征对肺结节的表达能力不强,生成的哈希码检索效果不佳。文中提出一种基于医学征象和卷积神经网络的肺结节CT图像哈希检索方法。首先,依据肺结节的9种征象取值,构造训练集准确的哈希码;其次,利用卷积神经网络和主成分分析法提取肺结节的重要语义特征,并结合训练集准确的哈希码反向求解哈希函数;最后,提出一种基于自适应比特位的检索方法,实现待查询肺结节图像的快速检出。通过对数据集进行实验和分析,证实了本文方法在肺结节图像检索过程中取得了较高的准确率和检索精度。