外文翻译--医学图像水印基准

1、外文翻译-医学图像水印基准 江西理工大学应用科学学院毕业设计论文外文资料翻译请注意各个局部我写的批注:首页请严格按照模板书写,横线要上下对齐 系 : 信息工程系 专 业: 网络工程 班 级:071 姓 名:邢星 学 号:38 附 件: 1.外文资料翻译译文;2.外文原文。 指导教师评语:签名: 年 月 日 注:请将该封面与附件装订成册。附件1 外文资料翻译译文:中文的译文在前,英文原文在后,中文要求小四号字,段落单倍行距,最低要求:大题目、各章节题目和摘要必须根本正确和通顺,英文原文中图和表、参考文献可以不翻译 医学图像水印基准 摘要-嵌入EPR的医疗图像可以用于传输,存储或者远距离的医学应用

2、。这需要一个特定的标准去评价嵌入EPR数据到医疗图像的数字水印技术。目前,没有存在的基准能去解决这个问题。水印系统还没有一个被普遍接受的性能指标。本文提出了一种基准去评价医学图像水印和数据隐藏技术。 数字图像水印的一个应用是在医疗图像中隐藏患者的数据。病人的数据是电子格式的被称为电子遍历(EPR)。 所有的工作报告以数据的形式隐藏在医疗图像中去验证和隐藏EPR。以不同形式附加EPR的医疗图像可以被发送给世界各地的临床医生去诊断病情。在医疗图像中嵌入EPR将会节省医院信息系统的存储空间,增强患者数据的机密性并且节约传输所需的带宽。显然这个技术将会减少诊断的花费。这个系统可以用数字水印技术确保所需

3、要的高等级平安性。 医疗图像数字水印缺乏系统的标准和条例。医疗图像数字水印团体普及世界各地,他们需要一个标准去交换全局的信息。当前流行的基准集中于细微评价和在传统非医疗图像的退化处理。它们不需要为特定的医疗图像类型提供一个评价方案体系或者为了从医疗图像处理中产生典型的退化。 早期的医疗图像数字水印技术几乎集中于主要两个方面:1.篡改检测和验证,2.在医疗图像中嵌入EPR。篡改检测水印用于识别进行操作的医疗图像。EPR数据可以利用空间域技术以及改造域技术嵌入进医疗图像。空间域水印技术有退化的倾向。嵌入技术必须无损因为在医疗应用有严格的高质量要求。嵌入的比特数量应该足够大以便于临床医生可以写他们的

4、诊断报告。一些有效的水印技术被用来嵌入文本信息到医疗图像中,可以在2,3,4中发现。流行的基准:非常重要的可靠基准是Stirmark, Checkmark, Optimark 和 Certimark。所有这些为评估各种类型平台的数字水印的基准方法有着共同的低效率。这给所有类型的图像水印研究设计一个基准留出了空间。 一个理想的基准程序应包括审查水印系统参数的相互依赖关系,它应明确优化权衡水印各方面的约束。使用一个特定的应用来评估这些参数多方面的性能度量。 数字水印的需求比方不可见性,容量和鲁棒性,它们互相冲突。因此在这些参数中必须仔细权衡。适宜的评估必须在一定程度上确保所有的选择符合需求。医疗图

5、像水印的评估方法不同于其他的基准因为有下面的限制。3.1 图像覆盖 基准包含不同大小一定数量的覆盖图像。医疗图像可以在不同的形式中使用比方CT,MRI,US和X光。医院信息系统包含综合医疗图像数据库和检索系统,可以让医生在任何时候去浏览患者的图像。这种系统允许医学图像以不同的方式被纳入到DICOM标准的图像数据库效劳器。数字水印可以在不改变图像大小或格式的情况下不知不觉的嵌入信息。所以医疗图像数字水印应该遵循DICOM格式。3.2 容量 尽管水印的容量是用比特每像素表达的,但是更方便的单位可以普遍适用于隐藏在医疗图像中的EPR文本数据称为最大数目的嵌入式字符(MNEC)。医疗图像数字水印的容量

6、必须尽可能的大。这就要消除对注解隐藏,消息验证,初次信息报告和详细诊断报告可用空间的限制。3.3 不可见性 水印后图像质量评价是为了测量水印之后的图像失真量。峰值信号信噪比(PSNR)和均方误差(MSE)是最广泛的用于客观评估图像质量/失真指标,但是它们没有很好的和质量评估进行关联。然而,覆盖图像的某一局部可以有效的标记水印的存在。错误的信号可以被人眼当做视觉质量评估的噪声。HVS的重要隐蔽效果将在下面的局部中进行解释。视觉遮蔽:当一个图像的组件是频率和方向时,图像的组件在人眼中将会不可见。这个重要的遮蔽效果称为亮度屏蔽,比照屏蔽和纹理屏蔽。人眼对平滑区域的改变比对纹理结构区域的改变更为敏感。

7、纹理遮蔽的效果被局部频率的分发和纹理方向所决定。一个参数用来描述纹理遮蔽效果称为噪声可见函数(NVF)。结构相似测量:另一个可感知的度量用来模拟医疗图像的水印退化,被称为结构相似测量(SSIM)。图像质量评估以SSIM为根底基于一个事实,即是高度适应的人类视觉系统从视场提取结构信息。SSIM度量是为了测试医疗图像的相似性,因为SSIM更集中于局部而不是全局图像的一致性。沃森度量:覆盖图像的非常规化的区域和亮度的高度改变会掩饰水印的存在。这个现象是被沃森模型给定的。该模型的根本目的是使用图像块的相应灵敏度阈值来加权DCT系数。该阀值是亮度遮蔽和比照遮蔽的混合功能。沃森度量使用最小可绝差(JND)

8、单位来计算水印图像的感知错误。3.4 感兴趣区域(ROI) 一个重要的考虑因素是使医学图像嵌入水印让医学图像包含感兴趣区域(ROI)。在医疗图像中,ROI包括重要的特征信息并且处理不能有任何失真。ROI通常在空间域中被选择。在空间域水印技术中,非感兴趣局部的像素可以直接被改变。Capacity-NVF-ROI Measure:水印的容量被认为是使用低可见性的错误嵌入进特殊覆盖图像的比特的数量。因此容量应该与图像的内容联系起来。覆盖图像的容量被评估为 CW?21+2/ n22是MWI的方差,n2是方差的噪音,W取决于像素的数量。如果一个图像大小为N×N,WN×N/2。3.5

9、攻击 系统性能的评估基准是在存储和医疗图像的传输期间的典型处理操作。各种类型的噪音在传输过程中通常会降低医疗图像,并且整体噪声可以作为高斯建模。噪声归因于图像长期的存储被作为斑点模型。3.6 鲁棒性 各种医疗图像水印鲁棒性的处理操作可以在嵌入的信息和萃取的信息中使用比特误码率(BER)来评估。BER的评估是通过改变每个降级过程的强度来实现的。 为了鉴定覆盖图像的噪声可见区域,每个像素会计算NVF数值。首先为了计算NVF数值会使用一个3×3的邻域来计算局部方差。128×128大小,8-bit灰阶的MRI图像心脏磁共振图像被用作覆盖图片。该NVF图像包含了相应的覆盖图像,如图1

10、所示。结果发现,NVF值在边缘和纹理局部接近0,而图像的平滑局部接近于1。 图.1 覆盖图像和它的NVF示意图 在LSB技术中,覆盖图像中每个像素的最低有效位可以使用水印来修改。在LSB平面的有效比特总数为16384比特。如此多数量的比特足以满足医疗图像的EPR数据隐藏的容量需求。这个覆盖图像的LSB平面的分析暴露了LSB平面包含了大量的冗余。EPR数据中的每个字符被7个比特编码并且在LSB平面的冗余位上印上水印。图.2 LSB平面内水印的失真 可以进一步提升容量通过在高阶位平面插入水印。图2说明了在覆盖图像中失真的发生当在6个平面中插入了3456比特的水印,然后可以看到从第四个平面开始失真变

11、的可见。测量不同的细微变化在表1中显示。 LSB Plane SSIM PSNR dB Watson metric 1 0.98 49.4 0.028 2 0.92 43.3 0.056 3 0.84 37.4 0.110 4 0.75 31.4 0.210 5 0.65 25.6 0.394 6 0.56 19.8 0.726 4.1 视觉品质 Vs 容量 视觉品质 Vs 容量图表习惯于用来估计可隐蔽性的嵌入到图像覆盖范围内的最大字符数量(MNEC)。使用LSB方法,编码的文本信息以不同的方式嵌入到覆盖图像中。通过嵌入进不同数量的字符数来计算WPSNR数值。以不同方式得到的图像结果的平均值。

12、在40分贝下WPSNR数值确保水印的不可见性。研究发现,LSB的技术可确保覆盖图像的最低退化。 为了评估WPSNR,误差(覆盖图像和水印图像的差异)按比例由每一个像素相应的NVF数值来评估。结果发现,与其他方式相比,在嵌入进一定数目字符的情况下,CT图像提供了最高等级的隐蔽性。这是由于CT图像临近的区域有鲜明的比照。 ROI的容量映射为水平清晰度是可以被计算出来的。最后,按照per1计算没有ROI的水平清晰度的容量.在盲水印方案中,两个不同的伪随机序列号被嵌入进ROI和非ROI区域,以便于水印检测器可以正确的区分ROI。正如预期的那样,嵌入进水平清晰度的比特减少的数量对应于ROI增加的大小,如

13、图(3)所示。 图.3 视觉品质与容量的退化4.2 视觉品质 Vs 攻击强度 使用视觉品质 Vs 攻击强度图表来说明水印视觉品质的退化。WPSNR的减少伴随着高斯噪声方差的增加。 用于测量噪声医学图像视觉退化的WPSNR使用比照灵敏度(CSF)作为权重因数。CSF的响应频率作为带通滤光器和被带通滤光器过滤的错误信号的参考,如图(4)所示。 图.4 视觉品质与攻击的退化4.3 比特错误率 Vs 攻击强度 比特错误率 Vs 攻击强度图表被用来发现水印对各种攻击的鲁棒性。在原始的水印和提取的水印中误码率随着斑点噪声方差的增加而增加。 很显然微波域中水印的容量明显小于空间域的,但是鲁棒性强于空间域。这

14、是因为使用空间域水印嵌入技术对像素的操作很敏感,然而在嵌入进微波次能带的时候它剩余了未改变的局部,如图(5)所示。 图.5 比特错误率和攻击的退化 本文为医疗图像的文本隐藏提出了一个基准。讨论了容量的界限,不可见性和鲁棒性。这些基准标准将会非常好的被全球的ROI图像数据隐藏团体用来设计和评估算法。作者参与了为这个领域的研究人员制定一个完整的基准数据表的工作。 参 考 文 献1 M. Kutter and F. A. P. Petitcolas, “A fair benchmark for image watermarking systems, Electronic Imaging 99. Se

15、curity and Watermarking of MultimediaContents, USA, vol. 3657, January 1999.2 S. Dandapat, Opas Chutatape and S. M. Krishnan,“Perceptual model based Data Embedding in a Medical Image, Proc. Int. Conf. on Image Processing, vol.4,pp. 2315-2318, October 2004.3 Xuanwen Luo, Qiang Cheng and Joseph Tan, “

16、A Lossless Data Embedding Scheme for Medical Images in Application of e-Diagnosis, Proc. 25th Annual Int.Conf. of the IEEE EMBS, Mexico, vol.1, pp.i-c, September 2003.4 A. Nikolaidis S. Tsekeridou A. Tefas V Solachidis, “A Survey on Watermarking Application Scenarios and Related Attacks, Proc. Int.

17、Conf. Image Processing,vol.3, pp.991-994, Oct. 2001.5 Rajendra Acharya U., P. Subhanna Bhat, Sathish Kumar and Lim Choo Min, “Transmission and storage of medical images with patient information, Journal ofComputers in Biology and Medicine, vol. 33, pp.303-310, 2003.6 Deepthi Anand and U.C. Niranjan,

18、 “Watermarking medical images with patient information, Proceedings of the 20th Annual International Conference of theIEEE Engineering in Medicine and Biology Society, vol. 2, pp.703-706, Nov.1998.7 G. Coatrieux, H. Maitre, B. Sankur, Y. Rolland and R.Collorec, “Relevance of Watermarking in Medical

19、Imaging,IEEE-EMBS Information Technology Applications in Biomedicine, pp.250-255, 2001.8 Xuanwen Luo and Qiang Cheng, “Health Information Integrating and Size Reducing, Proc. IEEE Nuclear Science Symposium, Medical Imaging Conference and Workshop of Room-Temperature Semiconductor Detectors, 2003.外文原

20、文:英文原文在后,要求从PDF格式的原文中粘贴形成word格式文章,字体新罗马小四号字,英文每个段落字体要两端对齐A Benchmark for Medical Image WatermarkingAbstract - The medical images with EPR embedded in it can be used for transmission, storage or telemedicine applications. There is a need of specific standards for the evaluation of watermarking techni

21、ques used for embedding EPR data on medical images. No existing benchmark addresses this issue. There are no universally accepted performance measures applicable for every watermarking system. In this paper a benchmark is proposed for the evaluation of medical image watermarking and data hiding tech

22、niques1. INTRODUCTION Hiding patient data in the medical image is one of the applications of digital image watermarking. The patient data in the electronic format is called Electronic patient record EPR. All works reported in data hiding in medical image are watermarking for authentication and EPR h

23、iding. The medical images of different modalities with EPR attached to them can be sent to the clinicians residing at any corner of the globe for the diagnosis. Embedding of EPR with medical images will save storage space of the Hospital Information System, enhance confidentiality of the patient dat

24、a and save the bandwidth required for transmission. Obviously this will reduce the cost of diagnosis. This kind of a system requires a high level of security, which can be ensured by using digital watermarking techniques Literature is devoid of a systematic norms or regulations for watermarking medi

25、cal images. Medical image watermarking communities around the world need a standard benchmark for the exchange of information globally. The currently popular benchmarks focus on evaluating imperceptibility and robustness under typical non-medical image degradation processes. They do not provide an e

26、valuation scheme applicable for specific medical image types, or for typical degradations arising from medical image processing2. MEDICAL IMAGE WATERMARKING TECHNIQUES Almost all the earlier works in medical image watermarking have focused mainly on two areas: 1. Tamper detection and authentication

27、and 2. Embedding EPR in medical images. Tamper detection watermarks are used for identifying manipulations done on medical images. EPR data can be embedded into the medical image using spatial domain techniques as well as transform domain techniques. Spatial domain watermarking techniques are prone

28、to degradations. The embedding technique must be lossless because of the stringent requirements on high quality in medical applications; however the number of embedded bits should be large enough for the clinicians to write their diagnosis report. Some of the available watermarking techniques used f

29、or embedding text information into medical images can be found in 2,3,4Popular Benchmarks: The important available benchmarks are Stirmark, Checkmark, Optimark and Certimark. All the these benchmarks share the common inefficiency of providing a platform for evaluating all kinds of image watermarking

30、 methods. This makes a room for research on devising a benchmark for all kinds of image watermarking3. A NOVEL BENCHMARK FOR MEDICAL IMAGE WATERMARKINGAn ideal benchmarking procedure should involve examining the set of mutually dependent parameters of the watermarking system and it should clearly op

31、timize the trade off between various constraints of watermarking. Various performance metrics are used to evaluate these parameters based on a specific applicationThe requirements of watermarking such as imperceptibility, capacity and robustness are hampering each other. Therefore, a trade off is es

32、sential between these parameters. A proper evaluation has to ensure that all the selected requirements are met to a certain level of assurance. The evaluation method for medical image watermarking techniques differs from the other benchmarks because of the following constraints The requirements of w

33、atermarking such as imperceptibility, capacity and robustness are hampering each other. Therefore, a trade off is essential between these parameters. A proper evaluation has to ensure that all the selected requirements are met to a certain level of assurance. The evaluation method for medical image

34、watermarking techniques differs from the other benchmarks because of the following constraints3.1 Cover Image Set The benchmark incorporates a number of cover images of varying size. The medical images are available in different modalities such as CT, MRI, US, and X-ray. The Hospital Information Sys

35、tem contains Integrated Medical Image Database and Retrieval System that enables doctors to browse patient images at any time. Such a system allows medical images in different modalities to be integrated into an image database server with the DICOM standard. Digital watermarking can imperceptibly em

36、bed messages without changing image size or format. So the watermarked medical image can conform to the DICOM format3.2 Capacity Though the capacity of watermark is expressed in bits per pixel, more convenient unit that can be generally applied to EPR text data hiding in Medical images is imum Numbe

37、r of Embedded Characters MNEC. For medical image watermarking, the capacity must be as high as possible. This is to remove a constraint of available space for hiding annotations, authentication message, first information report and detailed diagnosis report.3.3 Imperceptibility Measures The quality

38、assessment of an image after watermarking is done to measure the amount of distortion due to the watermarking. Peak Signal-to-Noise Ratio PSNR and Mean Squared Error MSE are the most widely used objective image quality/distortion metrics, but they are not correlating well with perceived quality meas

39、urement. However, certain portions of the cover image can effectively mask the presence of the watermark. The error signals that are visible to human eye need to be taken as noise for visual quality assessment. The important masking effects of HVS are explained in the following section1 Visual Maski

40、ng: When an image component is in the frequency and orientation, that image component becomes less conspicuous to the human eye. The important masking effects are Luminance Masking, Contrast masking and Texture masking. Human eye is less sensitive to changes in textured areas than in smooth areas. T

41、he texture masking effect is determined by local frequency distribution and texture direction. The texture masking effect is described using a parameter called Noise Visibility Function NVF2. Structural Similarity Measure: Another perceptual metric used to model the degradation of watermarked medica

42、l images is the Structural Similarity Measure SSIM. Image quality assessment based on SSIM is based on the fact that the HVS is highly adapted to extract structural information from the viewing field. SSIM metric is ideal for testing of similarities in medical images because it focuses on local rath

43、er than global image similarity3 .Watson Metric: Regions of non-regular and highly changing luminance in the cover image are able to mask the presence of watermark. This phenomenon is given by Watson model. The basic aim of the model is to weight the DCT coefficients in an image block by its corresp

44、onding sensitivity threshold. The threshold is a compound function of luminance masking and contrast masking. Watson metric is used to calculate the perceptual error in the watermarked image in Just Noticeable Difference JND units.3.4 Region of Interest ROI An important factor to be considered while

45、 watermarking medical images is that medical images contain Region of Interest ROI. In medical images, ROI is an area that contains diagnostically important information and must be processed without any distortion. The ROI is usually selected in the spatial domain. In spatial domain watermarking tec

46、hniques, the pixels in non-ROI parts can be modified directly1. Capacity-NVF-ROI Measure: The watermark capacity is considered as the number of bits that can be embedded into the particular cover image with low error visibility. Therefore the capacity measure must be associated with the content of i

47、mage. The capacity of the cover image is evaluated as, C W log21+2/ n2 1 Where2 is the variance of MWI andn2 is the noise variance and W depends upon the number of pixels. For an image of size N × N, W N × N/2.3.5 Attacks The benchmark evaluates the performance of the system under typical

48、processing operations during storage and transmission of medical images. Various types of noises usually degrade medical images during transmission and the overall noise can be modeled as Gaussian. The noise due to long-term storage of the image is modeled as speckle noise3.6 Robustness Measure The

49、robustness of the watermark to various medical image-processing operations can be evaluated using the Bit Error Rate BER between the embedded message and the extracted message. The BER is evaluated by varying the strength of each degradation process.4. RESULTS AND DISCUSSION In order to identify the

50、 regions of noise visibility in the cover images, NVF values were calculated at each pixel. First local variance was measured using a 3×3 neighborhood in order to calculate NVF values. 128×128 size, 8-bit gray scale MRI image of heart was used as the cover image. The NVF image obtained cor

51、responds to the cover image is shown in Fig. 1. It was found that NVF values were close to 0 in edges and textured portions, whereas it was close to 1 in flat portions of the image. Fig.1 Cover image and its NVF map In the LSB technique, the least significant bit of each pixel in the cover image is

52、modified using the watermark. The total number of bits available in the LSB plane was 16384 bits. This much amount of bits is sufficient to meet the capacity requirements of EPR data hiding in medical images. The analysis of LSB plane of the cover image reveals that the LSB plane contains a large amount of redundancy. Each character in the EPR data is encoded using 7-bits and watermarked into the redundant bits of LSB planeFig.2 Distortion due to watermarking in LSB planes The capacity ca