应用网格和水印技术对基于内容的肝脏CT图像检索的研究
|
摘要
随着医院数字化的发展和PACS的普及,医学影像数据急剧膨胀。人们对基于内容的图像检索(Content Based Image Retrieval, CBIR)技术开展了大量研究以解决日益增长的医学影像数据的检索问题。然而,时至今日CBIR技术尚未在临床上广泛应用,这与CBIR技术尚有许多有待完善之处有很大关系。
本文提出了应用网格和水印的肝脏CT图像基于内容检索(Content Based Hepatic-CT Image Retrieval using Grid and Watermarking, CBHIRuGW)系统,旨在提升CBIR在查准率、信息安全、检索效率和数据管理等方面的性能,从而提高医学图像信息的利用率,服务于临床、研究和教学。CBHIRuGW系统由存储子系统和检索子系统两部分组成;每个子系统分别由三大模块组成:即图像特征提取/CBIR检索模块、数字水印编码/解码模块、数据网格/计算网格模块。本文围绕这三组模块进行了相关研究:在CBIR模块组,本文提出了基于查询图像分类的图像检索方案以提高系统检索准确率;在水印模块组,利用无损水印技术,提升了系统信息安全和检索效率;在网格模块组,利用网格平台,进一步提升了系统检索效率,并简化了医学数据管理。关于这三组模块的详细叙述,即本文的主要研究内容如下:
第一,本文对提升检索系统的查准率进行了研究。在CBIR模块组,本文提出基于查询图像分类的图像检索方案以提高检索准确率。在根据查询图像检索相似图像时,采用某种图像分类方法,将查询图像和数据库中的图像组合在一起进行分类,并将数据库中与查询图像属于同一个类别的图像组成简化数据库;然后,采用CBIR检索方法,从该简化数据库(而不是原始数据库)检索图像。实验表明,通过增加分类处理这一环节,可以在一定程度上提高系统的检索准确率。本文首先采用了支持向量机分类方法用于肝脏CT图像检索,实验表明检索准确率有所提升。随后,根据肝脏CT图像的特点,本文提出了三级阈值分类方法,并将其用于肝脏CT图像检索,实验表明检索准确率获得了进一步提升。
第二,本文对检索系统的信息安全和检索效率进行了研究。在水印模块组,将数字水印技术应用于检索系统,以解决图像完整性验证、敏感信息加密等安全性问题以及提升图像相关信息检索效率问题。利用数字水印技术,将各种相关信息(包括患者隐秘信息、肝脏CT图像的Hash值、肝脏和病灶的形态特征,以及病灶区域的位置和纹理信息)以及其它诊断信息等作为水印负载嵌入图像,可以为检索系统带来一系列的好处:首先,水印特殊的编码以及密码保护方式可以为图像信息提供完整性验证以及设置多级读取权限,从而在一定程度上确保了医学数据及其相关信息的安全性,扩展了医学图像的应用范围;其次,由于图像的各种相关信息作为水印嵌入图像,在检索的过程中,一方面可以省去相关信息检索的操作,节约手工操作时间以及额外的系统响应时间,另一方面可以降低网络传输的负载,减少网络传输时间,从而提升图像相关信息的检索效率,这在网络拥塞情况下效果尤其明显;再次,ROI指示性水印(可见水印)可以为检索用户提供一个读片参考;最后,由于图像的各种相关信息作为水印嵌入图像,不再作为单独文件另外存储,在一定程度上简化了图像及其相关信息的管理。针对当前一些水印技术的不足之处,本文分别提出了基于分块排序和基于阈值控制的可扩展差值添加水印方法,从而提高了不同负载情况下的水印性能,降低了含水印图像的失真度。
第三,本文对检索系统的检索效率和图像管理进行了研究。在网格模块组,将网格计算技术应用于检索系统,以改善图像数据管理工作和提升系统检索效率。通过整合实验室闲置计算机,构建实验室网格平台对此方案进行了模拟实验:在数据管理方面,通过数据网格的功能模块,系统实现了对PACS系统进行无缝备份和恢复功能;在计算能力方面,利用网格提供的虚拟计算平台,系统对检索过程的最后一个环节(图像刚性配准)进行并行计算,从而大大提升了系统的检索效率。
With the development of hospital digitalization and popularity of PACS, medical data expand rapidly. CBIR (Content Based Image Retrieval) was focused for a few years in order to resolve the retrieval issues of medical images. However, CBIR is not applied widely in hospital, for it is still not perfect enough for clinical application.
A content-based hepatic CT image retrieval system using grid computing and watermarking technology, CBHIRuGW, is proposed for the retrieval of hepatic CT images, which aims to improve the performance of retrieval precision, data management, retrieval efficiency and patient’s data protection. This system increases the utilization of medical image information in diagnostics, teaching and research. CBHIRuGW is composed of storage and retrieval subsystem, each is composed of three groups separately: image features extracting/CBIR group, digital watermarking encoding/decoding group, data/computing grid group. Within CBIR group, a query image classification based image retrieval method is proposed to improve the retrieval precision; within watermarking group, system data security and retrieval efficiency are advanced with using lossless digital watermarking technology; within grid group, The performance of data management and retrieval efficiency are upgraded with using
grid computing technology. The details of these groups are discussed as follows: Firstly, a query image classification based image retrieval method is proposed. When retrieving images based on sample, images in the database are classified. Those belonging to the same class as the query sample are grouped as the simplified database and image retrieval is limited within the simplified database. At first, the support vector machine, SVM, was used for classification and the precision of retrival with SVM was better than that without the SVM. Then three level threshold classification method, 3-L was proposed for the hepatic CT images and experiments showed that the precision of retrival with 3-L was better than that with SVM.
Secondly, Information security and retrieval efficiency were dissussed to protect the patient’s private information, promote the retrieval efficiency of an image’s revelant information and simplify the data management. Using digital watermarking, all kinds of related information (such as hash value, morphological characteristics, location of the regional focus, texture information and patient’s information) is embedded in the image as watermarking, which has many advantages: Firstly, the special coding and password protection of watermarking enable image integrity verification and multi-levels read permissions, which ensures the security of medical data and expands the application of medical images. Secondly, since all relevant information is embedded within medical image, the operation of retrieving relevant information is not required during the retrieval processing, thus the time for manual operation and additional system costs is saved; morover, the network load is reduced and thus the network transmission time is reduced. As a result of these factors, the retrieval efficiency is improved. Finally, because all relevant information is no longer stored as a separate file but embedded into the medical image, the management of the relevant information is simplified. In order to improve the watermark performance and reduce distortion under low and high load conditions, the block sorting based and differences expansion threshold controlled embedding method are proposed.
Thirdly, retrieval efficiency and data management were discussed in the paper. The grid computing technology was applied to CBHIRuGW to improve the performance of data management and retrieval efficiency. The platform was simulated by integrating the idle computers in the laboratory. With the platform, the performance of the system's data management and computing is improved. In data management, the system can perform seamless storage and backup management for the PACS system with help of the functional module of data grid. In calculating abilities, use of virtual grid computing platforms, the system can process the last step of the image retrieval - the rigid registration of images in parallel, thus greatly enhancing the efficiency of the system crawl.
本文提出了应用网格和水印的肝脏CT图像基于内容检索(Content Based Hepatic-CT Image Retrieval using Grid and Watermarking, CBHIRuGW)系统,旨在提升CBIR在查准率、信息安全、检索效率和数据管理等方面的性能,从而提高医学图像信息的利用率,服务于临床、研究和教学。CBHIRuGW系统由存储子系统和检索子系统两部分组成;每个子系统分别由三大模块组成:即图像特征提取/CBIR检索模块、数字水印编码/解码模块、数据网格/计算网格模块。本文围绕这三组模块进行了相关研究:在CBIR模块组,本文提出了基于查询图像分类的图像检索方案以提高系统检索准确率;在水印模块组,利用无损水印技术,提升了系统信息安全和检索效率;在网格模块组,利用网格平台,进一步提升了系统检索效率,并简化了医学数据管理。关于这三组模块的详细叙述,即本文的主要研究内容如下:
第一,本文对提升检索系统的查准率进行了研究。在CBIR模块组,本文提出基于查询图像分类的图像检索方案以提高检索准确率。在根据查询图像检索相似图像时,采用某种图像分类方法,将查询图像和数据库中的图像组合在一起进行分类,并将数据库中与查询图像属于同一个类别的图像组成简化数据库;然后,采用CBIR检索方法,从该简化数据库(而不是原始数据库)检索图像。实验表明,通过增加分类处理这一环节,可以在一定程度上提高系统的检索准确率。本文首先采用了支持向量机分类方法用于肝脏CT图像检索,实验表明检索准确率有所提升。随后,根据肝脏CT图像的特点,本文提出了三级阈值分类方法,并将其用于肝脏CT图像检索,实验表明检索准确率获得了进一步提升。
第二,本文对检索系统的信息安全和检索效率进行了研究。在水印模块组,将数字水印技术应用于检索系统,以解决图像完整性验证、敏感信息加密等安全性问题以及提升图像相关信息检索效率问题。利用数字水印技术,将各种相关信息(包括患者隐秘信息、肝脏CT图像的Hash值、肝脏和病灶的形态特征,以及病灶区域的位置和纹理信息)以及其它诊断信息等作为水印负载嵌入图像,可以为检索系统带来一系列的好处:首先,水印特殊的编码以及密码保护方式可以为图像信息提供完整性验证以及设置多级读取权限,从而在一定程度上确保了医学数据及其相关信息的安全性,扩展了医学图像的应用范围;其次,由于图像的各种相关信息作为水印嵌入图像,在检索的过程中,一方面可以省去相关信息检索的操作,节约手工操作时间以及额外的系统响应时间,另一方面可以降低网络传输的负载,减少网络传输时间,从而提升图像相关信息的检索效率,这在网络拥塞情况下效果尤其明显;再次,ROI指示性水印(可见水印)可以为检索用户提供一个读片参考;最后,由于图像的各种相关信息作为水印嵌入图像,不再作为单独文件另外存储,在一定程度上简化了图像及其相关信息的管理。针对当前一些水印技术的不足之处,本文分别提出了基于分块排序和基于阈值控制的可扩展差值添加水印方法,从而提高了不同负载情况下的水印性能,降低了含水印图像的失真度。
第三,本文对检索系统的检索效率和图像管理进行了研究。在网格模块组,将网格计算技术应用于检索系统,以改善图像数据管理工作和提升系统检索效率。通过整合实验室闲置计算机,构建实验室网格平台对此方案进行了模拟实验:在数据管理方面,通过数据网格的功能模块,系统实现了对PACS系统进行无缝备份和恢复功能;在计算能力方面,利用网格提供的虚拟计算平台,系统对检索过程的最后一个环节(图像刚性配准)进行并行计算,从而大大提升了系统的检索效率。
With the development of hospital digitalization and popularity of PACS, medical data expand rapidly. CBIR (Content Based Image Retrieval) was focused for a few years in order to resolve the retrieval issues of medical images. However, CBIR is not applied widely in hospital, for it is still not perfect enough for clinical application.
A content-based hepatic CT image retrieval system using grid computing and watermarking technology, CBHIRuGW, is proposed for the retrieval of hepatic CT images, which aims to improve the performance of retrieval precision, data management, retrieval efficiency and patient’s data protection. This system increases the utilization of medical image information in diagnostics, teaching and research. CBHIRuGW is composed of storage and retrieval subsystem, each is composed of three groups separately: image features extracting/CBIR group, digital watermarking encoding/decoding group, data/computing grid group. Within CBIR group, a query image classification based image retrieval method is proposed to improve the retrieval precision; within watermarking group, system data security and retrieval efficiency are advanced with using lossless digital watermarking technology; within grid group, The performance of data management and retrieval efficiency are upgraded with using
grid computing technology. The details of these groups are discussed as follows: Firstly, a query image classification based image retrieval method is proposed. When retrieving images based on sample, images in the database are classified. Those belonging to the same class as the query sample are grouped as the simplified database and image retrieval is limited within the simplified database. At first, the support vector machine, SVM, was used for classification and the precision of retrival with SVM was better than that without the SVM. Then three level threshold classification method, 3-L was proposed for the hepatic CT images and experiments showed that the precision of retrival with 3-L was better than that with SVM.
Secondly, Information security and retrieval efficiency were dissussed to protect the patient’s private information, promote the retrieval efficiency of an image’s revelant information and simplify the data management. Using digital watermarking, all kinds of related information (such as hash value, morphological characteristics, location of the regional focus, texture information and patient’s information) is embedded in the image as watermarking, which has many advantages: Firstly, the special coding and password protection of watermarking enable image integrity verification and multi-levels read permissions, which ensures the security of medical data and expands the application of medical images. Secondly, since all relevant information is embedded within medical image, the operation of retrieving relevant information is not required during the retrieval processing, thus the time for manual operation and additional system costs is saved; morover, the network load is reduced and thus the network transmission time is reduced. As a result of these factors, the retrieval efficiency is improved. Finally, because all relevant information is no longer stored as a separate file but embedded into the medical image, the management of the relevant information is simplified. In order to improve the watermark performance and reduce distortion under low and high load conditions, the block sorting based and differences expansion threshold controlled embedding method are proposed.
Thirdly, retrieval efficiency and data management were discussed in the paper. The grid computing technology was applied to CBHIRuGW to improve the performance of data management and retrieval efficiency. The platform was simulated by integrating the idle computers in the laboratory. With the platform, the performance of the system's data management and computing is improved. In data management, the system can perform seamless storage and backup management for the PACS system with help of the functional module of data grid. In calculating abilities, use of virtual grid computing platforms, the system can process the last step of the image retrieval - the rigid registration of images in parallel, thus greatly enhancing the efficiency of the system crawl.
引文
[1] Z. Jia, L. Amselag, and P. Gros, "Content-based image retrieval from a large image database," Pattern Recognition, vol. 41, no. 5, pp. 1479-1495, 2008.
[2] H. Lu, X. Xue, and Y. P. Tan, "Content-based image and video indexing and retrieval," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4429 LNAI Shanghai, 2007, pp. 118-129.
[3] Part and K. K. Seo, "Content-based image retrieval by combining genetic algorithm and support vector machine," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4669 LNCS Porto, 2007, pp. 537-545.
[4] Part, J. Ha, and H. Choi, "The image retrieval method using multiple features," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4705 LNCS Kuala Lumpur, 2007, pp. 981-991.
[5] K. Wu and K. H. Yap, "Fuzzy SVM for content-based image retrieval," IEEE Computational Intelligence Magazine, vol. 1, no. 2, pp. 10-16, 2006.
[6] P. T. Yap and R. Paramesran, "Content-based image retrieval using Legendre chromaticity distribution moments," IEE Proceedings: Vision, Image and Signal Processing, vol. 153, no. 1, pp. 17-24, 2006.
[7] J. L. Bosque, O. D. Robles, L. Pastor, et al., "Parallel CBIR implementations with load balancing algorithms," Journal of Parallel and Distributed Computing, vol. 66, no. 8, pp. 1062-1075, 2006.
[8] S. H. Kwok and J. L. Zhao, "Content-based object organization for efficient image retrieval in image databases," Decision Support Systems, vol. 42, no. 3, pp. 1901-1916, 2006.
[9] M. Flickner, H. Sawhney, W. Niblack, et al., "Query by image and video content: the QBIC system," Computer, vol. 28, no. 9, pp. 23-32, 1995.
[10] W. Niblack, R. Barber, W. Equitz, et al., "QBIC project: querying images by content, using color, texture, and shape," in Proceedings of SPIE - The International Society for Optical Engineering, San Jose, CA, USA, 1993, vol. 1908, pp. 173-187.
[11] J. R. Bach, C. Fuller, A. Gupta, et al., "Virage image search engine: an open framework for image management," in Proceedings of SPIE - The International Society for Optical Engineering, San Jose, CA, USA, 1996, vol. 2670, pp. 76-87.
[12] Y. Rui, T. S. Huang, and S. Mehrotra, "Content-based image retrieval with relevance feedback in MARS," in IEEE International Conference on Image Processing, Santa Barbara, CA, USA, 1997, vol. 2, pp. 815-818.
[13] V. E. Ogle and M. Stonebraker, "Chabot: retrieval from a relational database of images," Computer, vol. 28, no. 9, pp. 40-48, 1995.
[14] S. D. MacArthur, C. E. Brodley, A. C. Kak, et al., "Interactive content-based image retrieval using relevance feedback," Computer Vision and Image Understanding, vol. 88, no. 2, pp. 55-75, 2002.
[15] B. C. Ko and H. Byun, "Query-by-gesture: An alternative content-based image retrieval query scheme," Journal of Visual Languages and Computing, vol. 13, no. 4, pp. 375-390, 2002.
[16] A. Pentland, R. W. Picard, and S. Sclaroff, "Photobook: Content-based manipulation of image databases," International Journal of Computer Vision, vol. 18, no. 3, pp. 233-254, 1996.
[17] W. Y. Ma and B. S. Manjunath, "NeTra: A toolbox for navigating large image databases," Multimedia Systems, vol. 7, no. 3, pp. 184-198, 1999.
[18]高原,耿国华,刘晓宁, "一种基于遗传算法自学习的图像检索方法,"西北大学学报(自然科学版), vol. 35, no. 04, pp. 379-382, 2005.
[19]赵莹, "基于颜色分布的图像检索系统,"微计算机信息, no. 18, 2007.
[20]杨振亚,杨振东,王勇,王成道, "基于色彩共生矩阵的CBIR方法,"计算机工程与应用, vol. 43, no. 20, pp. 40-42,51, 2007.
[21]邱开金,肖国强,张为群, "基于块边缘特征直方图的图像检索,"计算机科学, vol. 33, no. 04, pp. 215-217, 2006.
[22]唐波,孙茂印, "基于分块颜色相关向量的图像检索算法,"微计算机应用, vol. 28, no. 06, pp. 561-565, 2007.
[23] S.-K. Chang and T. Kunii, "Pictorial data-base applications," IEEE Comput. , vol. 14, no. 11, pp. 13-21, 1981.
[24] P. G. B. Enser, "Pictorial information retrieval," J. Document., vol. 51, no. 2, p. 126—170, 1995.
[25] A. Gupta and R. Jain, "Visual information retrieval," Communications of the ACM, vol. 40, no. 5, pp. 71-79, 1997.
[26] T. S. H. Y. Rui, S.-F. Chang, "Image retrieval: past, present and future," in the International Symposium on Multimedia Information Processing, Taipei, Taiwan, 1997.
[27] Y. Rui, T. S. Huang, and S. F. Chang, "Image retrieval: Current techniques, promising directions, and open issues," Journal of Visual Communication and Image Representation, vol. 10, no. 1, pp. 39-62, 1999.
[28] A. W. M. Smeulders, M. Worring, S. Santini, et al., "Content-based image retrieval at the end of the early years," IEEE Transactions on Pattern Analysisand Machine Intelligence, vol. 22, no. 12, pp. 1349-1380, 2000.
[29] K. K. Seo, "An application of one-class support vector machines in content-based image retrieval," Expert Systems with Applications, vol. 33, no. 2, pp. 491-498, 2007.
[30] M. J. Huiskes and E. J. Pauwels, "Indexing, Learning and Content-Based Retrieval for Special Purpose Image Databases," in Advances in Computers. vol. 65, M. Zelkowitz, Ed., 2005, pp. 203-258.
[31] S. C. Cheng, T. C. Chou, C. L. Yang, et al., "A semantic learning for content-based image retrieval using analytical hierarchy process," Expert Systems with Applications, vol. 28, no. 3, pp. 495-505, 2005.
[32] C. R. Shyu, C. E. Brodley, A. C. Kak, et al., "ASSERT: A physician-in-the-loop content-based retrieval system for HRCT image databases," Computer Vision and Image Understanding, vol. 75, no. 1, pp. 111-132, 1999.
[33] P. M. Kelly, T. M. Cannon, and J. E. Barros, "Efficiency issues related to probability density function comparison," in Proceedings of SPIE - The International Society for Optical Engineering, San Jose, CA, USA, 1996, vol. 2670, pp. 42-49.
[34] H. D. Tagare, F. M. Vos, C. C. Jaffe, et al., "Arrangement: a spatial relation between parts for evaluating similarity of tomographic section," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 9, pp. 880-893, 1995.
[35] J. Z. Wang, "Pathfinder: multiresolution region-based searching of pathology images using IRM," Proceedings / AMIA . Annual Symposium. AMIA Symposium, pp. 883-887, 2000.
[36] R. Panchal and B. Verma, "Characterization of breast abnormality patterns in digital mammograms using auto-associaitor neural network," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Hong Kong, 2006, vol. 4234 LNCS - III, pp. 127-136.
[37] W. Cai, D. Feng, and R. Fulton, "Content-based retrieval of dynamic PET functional images," IEEE Transactions on Information Technology in Biomedicine, vol. 4, no. 2, pp. 152-158, 2000.
[38] C. Y. Han, H. Chen, L. He, et al., "A Web-based distributed image processing system," in Proceedings of SPIE - The International Society for Optical Engineering, Santa Clara, CA, 2003, vol. 5018, pp. 111-122.
[39] T. M. Lehmann, M. O. Gu?ld, C. Thies, et al., "Content-based image retrieval in medical applications for picture archiving and communication systems," in Proceedings of SPIE - The International Society for Optical Engineering, San Diego, CA, 2003, vol. 5033, pp. 109-117.
[40] Y. Tan, J. Zhang, Y. Hua, et al., "Content-based image retrieval in Picture Archiving and Communication System," in Progress in Biomedical Optics and Imaging - Proceedings of SPIE, San Diego, CA, 2006, vol. 6145.
[41] D. Zhang, Y. Yang, and Q. Q. Qin, "A fast image retrieval algorithm with multi-channel textural features in PACS," Wuhan University Journal of Natural Sciences, vol. 10, no. 5, pp. 847-850, 2005.
[42] H. Greenspan and A. T. Pinhas, "Medical image categorization and retrieval for PACS using the GMM-KL framework," IEEE Transactions on Information Technology in Biomedicine, vol. 11, no. 2, pp. 190-202, 2007.
[43] S. Batty, A. Blandford, J. Clark, et al., "Content based retrieval of lesioned brain images," in Proceedings of SPIE - The International Society for Optical Engineering, San Diego, CA, 2002, vol. 4685, pp. 128-136.
[44] S. Atnafu, R. Chbeir, and L. Brunie, "Content-based and metadata retrieval in medical image database," in Proceedings of the IEEE Symposium on Computer-Based Medical Systems, Maribor, 2002, pp. 327-332.
[45] I. El-Naqa, Y. Yang, N. P. Galatsanos, et al., "Content-based image retrieval for digital mammography," in IEEE International Conference on Image Processing, Rochester, NY, 2002, vol. 3.
[46] S. Antani, L. R. Long, and G. R. Thoma, "Content-based image retrieval for large biomedical image archives," Medinfo, vol. 11, no. Pt 2, pp. 829-833, 2004.
[47] B. F. Jones, G. Schaefer, and S. Y. Zhu, "Content-based image retrieval for medical infrared images," in Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, San Francisco, CA, 2004, vol. 26 II, pp. 1186-1187.
[48] L. H. Y. Tang, R. Hanka, and H. H. S. Ip, "A review of intelligent content-based indexing and browsing of medical images," Health Informatics J., vol. 5,p. 40—49, 1999.
[49] S. S. Li and T. G. Zhuang, "Content-based medical image retrieval," Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation, vol. 25, no. 2, pp. 95-98, 2001.
[50] G. Scott and C. R. Shyu, "Knowledge-driven multidimensional indexing structure for biomedical media database retrieval," IEEE Transactions on Information Technology in Biomedicine, vol. 11, no. 3, pp. 320-331, 2007.
[51] T. Deselaers, H. Mu?ller, P. Clough, et al., "The CLEF 2005 automatic medical image annotation task," International Journal of Computer Vision, vol. 74, no. 1, pp. 51-58, 2007.
[52] S. Lefevre, J. Holler, and N. Vincent, "A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval," Real-Time Imaging, vol. 9, no. 1, pp. 73-98, 2003.
[53]周杰,陈武凡, "医学图像检索分类的MOAB编码方案,"中国图象图形学报, vol. 09, no. 02, pp. 214-219, 2004.
[54]王勇,吕扬生, "基于纹理特征的超声医学图像检索,"天津大学学报, vol. 38, no. 01, pp. 57-60, 2005.
[55]赵晨光,庄天戈, "基于内容的医学图像检索"国外医学.生物医学工程分册, no. 02期2004.
[56]温智韡,吴效明,郭圣文, "基于模糊聚类的医学图像检索方法,"中国医学物理学杂志, vol. 24, no. 03, pp. 180-183,223, 2007.
[57]董健卫,黄榕波,马建华, "多进制小波在医学图像检索中的应用,"医疗卫生装备, vol. 28, no. 01, pp. 5-7,10, 2007.
[58] H. Muller, N. Michoux, D. Bandon, et al., "A review of content-based image retrieval systems in medical applications - Clinical benefits and future directions," International Journal of Medical Informatics, vol. 73, no. 1, pp. 1-23, 2004.
[59] W. Bender, F. J. Paiz, W. Butera, et al., "Applications for data hiding," IBM Systems Journal, vol. 39, no. 3-4, pp. 547-568, 2000.
[60] W. Bender, D. Gruhl, N. Morimoto, et al., "Techniques for data hiding," IBM Systems Journal, vol. 35, no. 3-4, pp. 313-335, 1996.
[61] H. Trichili, M. S. Bouhlel, and L. Kamoun, "A review of watermarking techniques: Applications, properties, and domains," Journal of Testing and Evaluation, vol. 31, no. 4, pp. 357-360, 2003.
[62] R. Van Schyndel, A. Tirkel, and C. Osborne, "A digital watermark," IEEE International Conference on Image Processing, (Austin, Texas), November 1994, pp. 86-90, 2004.
[63] "Special issue on identification and protection of multimedia information," Proc. IEEE, vol. 87, no. 7, pp. 1059-1276, 1999.
[64] "Special Issue on Copyright and Privacy Protection," IEEE J. Select. Areas Commun., vol. 16,pp. 452-593, 1998.
[65] Z. Bojkovic and D. Milovanovic, "Multimedia contents security: Watermarking requirements and techniques," Recent Advances in Communications and Computer Science, pp. 178-183, 2003.
[66] A. M. Eskicioglu, "Multimedia security in group communications: Recent progress in key management, authentication, and watermarking," Multimedia Systems, vol. 9, no. 3, pp. 239-248, 2003.
[67] F. Hartung and M. Kutter, "Multimedia watermarking techniques," Proceedings of the IEEE, vol. 87, no. 7, pp. 1079-1107, 1999.
[68] H. S. Sung and S. W. Kim, "An improvement of auto-correlation based video watermarking scheme using perceptual masking for motion," in Lecture Notesin Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3816 LNCS, 2005, pp. 410-419.
[69] J. R. Hernandez and F. Perez-Gonzalez, "Statistical analysis of watermarking schemes for copyright protection of images," Proceedings of the IEEE, vol. 87, no. 7, pp. 1142-1166, 1999.
[70]陈峥,姚宇红,王晓京, "一种基于二维图形码的数字水印技术,"计算机应用, vol. 26, no. 08, pp. 1998-2000, 2006.
[71]刘瑞祯,谭铁牛, "数字图像水印研究综述,"通信学报, vol. 21, no. 08, pp. 39-48, 2000.
[72]谢勍, "数字水印在指纹识别中的应用研究,"微计算机应用, vol. 27, no. 01, pp. 98-100, 2006.
[73] X. Guo and T.-g. Zhuang, "A Region-Based Lossless Watermarking Scheme for Enhancing Security of Medical Data," Journal of Digital Imaging.
[74]程卫东,黄继武, and刘红梅, "一种基于3D-DCT的彩色图像信息隐藏算法,"自动化学报, no. 02, 2003.
[75]许君一,熊昌镇,齐东旭, et al., "量化水印算法分析,"通信学报, no. 03, 2006.
[76] J. Fridrich, M. Goljan, and R. Du, "Invertible authentication," in Proceedings of SPIE - The International Society for Optical Engineering, San Jose, CA, 2001, vol. 4314, pp. 197-208.
[77] M. Goljan, J. Friedrich, and R. Du, "Distortion-free data embedding for images," Fourth International Information Hiding Workshop, 2001.
[78] M. U. Celik, G. Sharma, A. M. Tekalp, et al., "Lossless generalized-LSB data embedding," IEEE Transactions on Image Processing, vol. 14, no. 2, pp. 253-266, 2005.
[79] S. K. Yip, O. C. Au, H. M. Wong, et al., "Generalized lossless data hiding by multiple predictors," in Proceedings - IEEE International Symposium on Circuits and Systems, Kos, 2006, pp. 1426-1429.
[80] G. Xuan, J. Zhu, J. Chen, et al., "Distortionless data hiding based on integer wavelet transform," Electronics Letters, vol. 38, no. 25, pp. 1646-1648, 2002.
[81] Z. Ni, Y. Q. Shi, N. Ansari, et al., "Reversible data hiding," in Proceedings - IEEE International Symposium on Circuits and Systems, Bangkok, 2003, vol. 2.
[82] B. Yang, M. Schmucker, W. Funk, et al., "Integer DCT-based reversible watermarking for images using companding technique," in Proceedings of SPIE - The International Society for Optical Engineering, San Jose, CA, 2004, vol. 5306, pp. 405-415.
[83] Y. Hu, B. Jeon, Z. Lin, et al., "Analysis and comparison of typical reversiblewatermarking methods," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4283 LNCS Jeju Island, 2006, pp. 333-347.
[84] D. Coltuc and A. Tremeau, "Simple reversible watermarking schemes," in Proceedings of SPIE - The International Society for Optical Engineering, San Jose, CA, 2005, vol. 5681, pp. 561-568.
[85] D. Coltuc and J. M. Chassery, "Simple reversible watermarking schemes: Further results," in Proceedings of SPIE - The International Society for Optical Engineering, San Jose, CA, 2006, vol. 6072.
[86] J. Tian, "Reversible Data Embedding Using a Difference Expansion," IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, no. 8, pp. 890-896, 2003.
[87] J. Tian, "Reversible watermarking by difference expansion," ACM Multimedia 2002 Workshop W2 - Workshop on Multimedia and Security: Authentication, Secrecy, and Steganalysis, pp. 19-22, 1 December 2002 through 6 December 2002 2002.
[88] A. M. Alattar, "Reversible watermark using difference expansion of triplets," in IEEE International Conference on Image Processing, Barcelona, 2003, vol. 1, pp. 501-504.
[89] A. M. Alattar, "Reversible watermark using the difference expansion of a generalized integer transform," IEEE Transactions on Image Processing, vol. 13, no. 8, pp. 1147-1156, 2004.
[90] L. Kamstra and H. J. A. M. Heijmans, "Reversible data embedding into images using wavelet techniques and sorting," IEEE Transactions on Image Processing, vol. 14, no. 12, pp. 2082-2090, 2005.
[91] D. M. Thodi and J. J. Rodri?guez, "Reversible watermarking by prediction-error expansion," in Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, Lake Tahoe, NV, 2004, vol. 6, pp. 21-25.
[92] D. M. Thodi and J. J. Rodriguez, "Expansion embedding techniques for reversible watermarking," IEEE Transactions on Image Processing, vol. 16, no. 3, pp. 721-730, 2007.
[93] R. A. U, D. Anand, S. B. P, et al., "Compact storage of medical images with patient information," IEEE Transactions on Information Technology in Biomedicine, vol. 5, no. 4, pp. 320-323, 2001.
[94] X. Kong and R. Feng, "Watermarking medical signals for telemedicine," IEEE Transactions on Information Technology in Biomedicine, vol. 5, no. 3, pp. 195-201, 2001.
[95] X. Q. Zhou, S. L. Lou, and H. K. Huang, "Authenticity and integrity of digital mammographic images," in Proceedings of SPIE - The International Societyfor Optical Engineering, San Diego, CA, USA, 1999, vol. 3662, pp. 138-144.
[96] H. M. Chao, C. M. Hsu, and S. G. Miaou, "A data-hiding technique with authentication, integration, and confidentiality for electronic patient records," IEEE Transactions on Information Technology in Biomedicine, vol. 6, no. 1, pp. 46-53, 2002.
[97] F. Bao, R. H. Deng, B. C. Ooi, et al., "Tailored reversible watermarking schemes for authentication of electronic clinical atlas," IEEE Transactions on Information Technology in Biomedicine, vol. 9, no. 4, pp. 554-563, 2005.
[98] M. Engin, O. C?idam, and E. Z. Engin, "Wavelet transformation based watermarking technique for human Electrocardiogram (ECG)," Journal of Medical Systems, vol. 29, no. 6, pp. 589-594, 2005.
[99] Z. Zhou, H. K. Huang, and B. J. Liu, "Digital Signature Embedding (DSE) for medical image integrity in a data grid off-site backup archive," in Progress in Biomedical Optics and Imaging - Proceedings of SPIE, San Diego, CA, 2005, vol. 5748, pp. 306-317.
[100]都志辉,陈渝,刘鹏,网格计算:清华大学出版社, 2002.
[101]金海,袁平鹏,石柯,网格计算(第二版):电子工业出版社, 2004.
[102] K. Czajkowski , I. Foster , and C. Kesselman "Co-allocation services for computational Grids," in In 8th IEEE Internatioanl Symposium on High Performance Distributed Computing, Los Alamitos, CA, , 1999.
[103] K. Anstreicher, N. Brixius, J. P. Goux, et al., "Solving large quadratic assignment problems on computational grids," Mathematical Programming, Series B, vol. 91, no. 3, pp. 563-588, 2002.
[104]孙培德,胡月仙, "网格计算的研究进展及应用前景,"计算机时代, no. 01, pp. 1-5, 2003.
[105] J. M. Brady, D. J. Gavaghan, A. C. Simpson, et al., "eDiamond: a grid-enabled federated database of annotated mammograms," in Grid Computing: Making the Global Infrastructure a Reality, F. Berman, G. C. Fox, and A. J. G. Hey, Eds., 2003, pp. 923-943.
[106] S. R. Amendolia, F. Estrella, W. Hassan, et al., "MammoGrid: a service oriented architecture based medical grid application," in Grid and Cooperative Computing - GCC 2004. Third International Conference Proceedings, Wuhan, China, 2004, pp. 939-942.
[107] A. Amir, S. Srinivasan, and A. Efrat, "Search the audio, browse the video - A generic paradigm for video collections," Eurasip Journal on Applied Signal Processing, vol. 2003, no. 2, pp. 209-222, 2003.
[108] C. T. Yang, Y. L. Kuo, and C. L. Lai, "Designing computing platform for BioGrid," International Journal of Computer Applications in Technology, vol. 22, no. 1, pp. 3-13, 2005.
[109] C. Stark, B. J. Breitkreutz, T. Reguly, et al., "BioGRID: a general repositoryfor interaction datasets," Nucleic acids research, no. 34, 2006.
[110] C. H. Huang and S. Rajasekaran, "BioGrid*- Bridging life science and information technology," in 2005 IEEE International Symposium on Cluster Computing and the Grid, CCGrid 2005, Cardiff, Wales, 2005, vol. 1, pp. 450-454.
[111] C. H. Huang, V. Lanza, S. Rajasekaran, et al., "HealthGrid - Bridging life science and information technology," Journal of Clinical Monitoring and Computing, vol. 19, no. 4-5, pp. 259-262, 2005.
[112] V. Breton, K. Dean, T. Solomonides, et al., "The Healthgrid White Paper," Studies in health technology and informatics, vol. 112,pp. 249-321, 2005.
[113] I. C. Oliveira, J. L. Oliveira, J. P. Sanchez, et al., "Grid requirements for the integration of biomedical information resources for health applications," Methods of Information in Medicine, vol. 44, no. 2, pp. 161-167, 2005.
[114] Z. Yang and C. C. J. Kuo, "Survey on image content analysis, indexing, and retrieval techniques and status report of MPEG-7," Tamkang Journal of Science and Engineering, vol. 2, no. 3, pp. 101-118, 1999.
[115] B. M. Mehtre, M. S. Kankanhalli, and W. F. Lee, "Shape measures for content based image retrieval: A comparison," Information Processing and Management, vol. 33, no. 3, pp. 319-337, 1997.
[116] G. Mountrakis, A. Stefanidis, I. Schlaislch, et al., "Supporting quality-based image retrieval through user preference learning," Photogrammetric Engineering and Remote Sensing, vol. 70, no. 8, pp. 973-981, 2004.
[117] S. C. Horii, B. A. Levine, G. Goger, et al., "A comparison of case retrieval times: film versus picture archiving and communications systems," Journal of digital imaging : the official journal of the Society for Computer Applications in Radiology, vol. 5, no. 3, pp. 138-143, 1992.
[118] R. Malladi, J. A. Sethian, and B. C. Vemuri, "Shape modeling with front propagation: a level set approach," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 2, pp. 158-175, 1995.
[119] M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: Active contour models," International Journal of Computer Vision, vol. 1, no. 4, pp. 321-331, 1988.
[120] L. Vincent and P. Soille, "Watersheds in digital spaces: An efficient algorithm based on immersion simulations," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 6, pp. 583-598, 1991.
[121]赵晨光, "基于内容的肝脏CT图像检索的研究,"上海交通大学生物医学工程, 2005
[122] C. Brodley, A. Kak, C. Shyu, et al., "Content-based retrieval from medical image databases: a synergy of human interaction, machine learning and computer vision," Proceedings of the National Conference on Artificial Intelligence, pp. 760-767, 18 July 1999 through 22 July 1999 1999.
[123] W. W. Chu, "KMeD: A knowledge-based multimedia medical distributed database system," Information Systems, vol. 20, no. 2, pp. 75-96, 1995.
[124] B. S. Manjunath, "Gabor wavelet transform and application to problems in computer vision," in 26th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, 1992, pp. 796-800.
[125] J. Z. Wang, J. Li, and G. Wiederhold, "SIMPLIcity: Semantics-sensitive integrated Matching for Picture Libraries," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 9, pp. 947-963, 2001.
[126] A. K. Jain and A. Vailaya, "Image retrieval using color and shape," Pattern Recognition, vol. 29, no. 8, pp. 1233-1244, 1996.
[127] R. C. Gonzalez and R. E. Woods, "Segmentation by Morphological Watersheds," in Digital image Processing (Second Edition), 2002, pp. 617-621.
[128] L. Najman and M. Schmitt, "Geodesic saliency of watershed contours and hierarchical segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 12, pp. 1163-1173, 1996.
[129] F. Meyer, "An overview of morphological segmentation," International Journal of Pattern Recognition and Artificial Intelligence, vol. 15, no. 7, pp. 1089-1118, 2001.
[130]赵晨光,宋利伟, and庄天戈, "基于分水岭区域差异性的多级图像分割,"上海交通大学学报, vol. 38, no. 9, pp. 1489-1491, 2004.
[131] A. Mehnert and P. Jackway, "An improved seeded region growing algorithm," Pattern Recognition Letters, vol. 18, no. 10, pp. 1065-1071, 1997.
[132] A. Bleau and L. J. Leon, "Watershed-based segmentation and region merging," Computer Vision and Image Understanding, vol. 77, no. 3, pp. 317-370, 2000.
[133] R. Lotufo and W. Silva, "Minimal Set of Markers for the Watershed Transform," Proceedings of International Symposium on mathematical morphology (ISMM2002), 2000.
[134] M. Wei, Y. Zhou, and M. Wan, "A fast snake model based on non-linear diffusion for medical image segmentation," Computerized Medical Imaging and Graphics, vol. 28, no. 3, pp. 109-117, 2004.
[135] X. M. Pardo, M. J. Carreira, A. Mosquera, et al., "A snake for CT image segmentation integrating region and edge information," Image and Vision Computing, vol. 19, no. 7, pp. 461-475, 2001.
[136] J. M. Pardo, D. Cabello, and J. Heras, "A snake for model-based segmentation of biomedical images," Pattern Recognition Letters, vol. 18, no. 14, pp. 1529-1538, 1997.
[137] C. Xu and J. L. Prince, "Snakes, shapes, and gradient vector flow," IEEE Transactions on Image Processing, vol. 7, no. 3, pp. 359-369, 1998.
[138] S. Suthaharan, "Image and edge detail detection algorithm for object-basedcoding," Pattern Recognition Letters, vol. 21, no. 6-7, pp. 549-557, 2000.
[139] H. Tang, E. X. Wu, Q. Y. Ma, et al., "MRI brain image segmentation by multi-resolution edge detection and region selection," Computerized Medical Imaging and Graphics, vol. 24, no. 6, pp. 349-357, 2000.
[140] S. Sanjay-Gopal and T. J. Hebert, "Bayesian pixel classification using spatially variant finite mixtures and the generalized EM algorithm," IEEE Transactions on Image Processing, vol. 7, no. 7, pp. 1014-1028, 1998.
[141] S. L. Sclove, "APPLICATION OF THE CONDITIONAL POPULATION-MIXTURE MODEL TO IMAGE SEGMENTATION," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-5, no. 4, pp. 428-433, 1983.
[142] D. M. Titterington, A. F. M. Smith, and U. E. Makov, "Statistical Analysis of Finite Mixture Distributions," New York: John Wiley., 1985.
[143] M. Porat and Y. Y. Zeevi, "Localized texture processing in vision: analysis and synthesis in the Gaborian space," IEEE Transactions on Biomedical Engineering, vol. 36, no. 1, pp. 115-129, 1989.
[144] M. Porat and Y. Y. Zeevi, "The Generalized Gabor Scheme Of Image Representation In Biological And Machine Vision," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, no. 4, pp. 452-468, 1988.
[145] A. K. Jain and F. Farrokhnia, "Unsupervised texture segmentation using Gabor filters," Pattern Recognition Letters, vol. 24, no. 12, pp. 1167-1186, 1991.
[146] D. Dunn and W. E. Higgins, "Optimal Gabor filters for Texture Segmentation," IEEE Transactions on Image Processing, vol. 4, no. 7, pp. 947-964, 1995.
[147] D. Dunn, W. E. Higgins, and J. Wakeley, "Texture segmentation using 2-D Gabor elementary functions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 2, pp. 130-149, 1994.
[148] D. Gabor, "Theory of communication," Inst. Elec. Eng., vol. 93,pp. 429-459, 1946.
[149] B. S. Manjunath and W. Y. Ma, "Texture features for browsing and retrieval of image data," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 837-842, 1996.
[150] J. G. Daugman, "Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters.," J. Opt. Soc Am. A pp. 1160-1169, 1985.
[151] J. R. Movellan, "tutorial on gabor filters," 1996.
[152] D. A. Pollen and S. F. Ronner, "Visual cortical neurons as localized spatial frequency filters," IEEE Transactions on Systems, Man and Cybernetics, vol. 13, no. 5, pp. 907-916, 1983.
[153] B. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers," in Proceedings of the Fifth Annual ACM Workshop onComputational Learning Theory, Pittsburgh, PA, USA, 1992, pp. 144-152.
[154] C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
[155] V. Vapnik, The nature of statistical learning theory. New York: Springer-Verlag, 1995.
[156] C.-C. Chang and C.-J. Lin, "LIBSVM : a library for support vector machines," 2001.
[157] C. Zhao, H. Cheng, and T. Zhuang, "Hepatic CT image query using Gabor features," Chinese Optics Letters, vol. 2, no. 7, pp. 383-385, 2004.
[158] A. Giakoumaki, S. Pavlopoulos, and D. Koutsouris, "Multiple image watermarking applied to health information management," IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 4, pp. 722-732, 2006.
[159] C. W. Honsinger, P. W. Jones, M. Rabbani, et al., "Lossless recovery of an original image containing embedded data," 2001.
[160] B. Macq, "Lossless multiresolution transform for image authenticating watermarking," Proc. EUSIPCO, Tampere, Finland, September 2000, 2000.
[161] J. Fridrich, M. Goljan, and R. Du, "Lossless data embedding for all image formats," in Proceedings of SPIE - The International Society for Optical Engineering, San Jose, CA, 2002, vol. 4675, pp. 572-583.
[162] T. Kalker and F. M. J. Willems, "Capacity bounds and constructions for reversible data-hiding," in Proceedings of SPIE - The International Society for Optical Engineering, Santa Clara, CA, 2003, vol. 5020, pp. 604-611.
[163] R. L. Rivest, "The MD4 message digest algorithm," RFC, vol. 1186,1990.
[164] R. Rivest, "The MD5 message-digest algorithm," RFC, vol. 1321,1992.
[165] R. L. Rivest, A. Shamir, and L. Adleman, "method for obtaining digital signatures and public-key cryptosystems," Communications of the ACM, vol. 21, no. 2, pp. 120-126, 1978.
[166] I. Foster, C. Kesselman, and S. Tuecke, "The anatomy of the grid: Enabling scalable virtual organizations," International Journal of High Performance Computing Applications, vol. 15, no. 3, pp. 200-222, 2001.
[167] E. Mangonaux, X. S. Zhou, and D. Comaniciu, "A comparative study of dissimilarity measures for histogram-based tracking in echocardiography," in Proceedings of SPIE - The International Society for Optical Engineering, San Diego, CA, 2004, vol. 5370 III, pp. 1509-1518.
[168] A. Parasyn, R. M. Hanson, J. K. Peat, et al., "A comparison between digital images viewed on a picture archiving and communication system diagnostic workstation and on a PC-based remote viewing system by emergency physicians," Journal of Digital Imaging, vol. 11, no. 1, pp. 45-49, 1998.
[169] B. Allcock, J. Bester, J. Bresnahan, et al., "Data management and transfer in high-performance computational grid environments," Parallel Computing, vol.28, no. 5, pp. 749-771, 2002.
[170]章鲁,李三立, "生物医学网格及其应用,"上海第二医科大学学报, vol. 25, no. 11, pp. 1177-1183, 2005.
[171] H. Duque, J. Montagnat, J. M. Pierson, et al., "DM2 : A distributed medical data manager for grids," in Ccgrid 2003: 3rd Ieee/Acm International Symposium on Cluster Computing and the Grid, Proceedings, F. Titsworth and D. Azada, Eds., 2003, pp. 606-611.
[172] K. Hassan, T. Tweed, and S. Miguet, "A multi-resolution approach for content-based image retrieval on the grid: Application to breast cancer detection," Methods of Information in Medicine, vol. 44, no. 2, pp. 211-214, 2005.
[173] D. Slik, O. Seiler, T. Altman, et al., "Scalable fault tolerant image communication and storage grid," in Proceedings of SPIE - The International Society for Optical Engineering, San Diego, CA, 2003, vol. 5033, pp. 36-47.
[174] X. Zhang and J. Zhang, "Data grid - A distributed solution to PACS," in Progress in Biomedical Optics and Imaging, San Diego, CA, 2004, vol. 5, pp. 334-344.
[175] "The OGSA-DAI project," in http://www.ogsa-dai.org.uk/ 2005.
[176] I. Kojima and S. M. Pahlevi, "Design and implementation of OGSA-WebDB," in http://www.nesc.ac.uk/events/GGF10-DA/ 2004.
[177] "OGSA-DQP," in http://www.ogsa-dai.org.uk/dqp/ 2004.
[178] E. Bagarinao, L. Sarmenta, Y. Tanaka, et al., "The Application of Grid Computing to Real-Time Functional MRI Analysis," in Parallel and Distributed Processing and Applications, 2004, pp. 290-302.
[179] T. C. Pan, M. N. Gurcan, S. A. Langella, et al., "Informatics in radiology: GridCAD: grid-based computer-aided detection system," Radiographics : a review publication of the Radiological Society of North America, Inc, vol. 27, no. 3, pp. 889-897, 2007.
[180] M. C. Oliveira, W. Cirne, and P. M. de Azevedo Marques, "Towards applying content-based image retrieval in the clinical routine," Future Generation Computer Systems, vol. 23, no. 3, pp. 466-474, 2007.
[181] M. Redmond, E. Brodsky, Y. H. Hu, et al., "The 4D cluster visualization project," in Proceedings of SPIE - The International Society for Optical Engineering, San Diego, CA, 2004, vol. 5367, pp. 28-38.
[182] H. Pohjonen, P. Ross, J. G. Blickman, et al., "Pervasive access to images and data - The use of computing grids and mobile/wireless devices across healthcare enterprises," IEEE Transactions on Information Technology in Biomedicine, vol. 11, no. 1, pp. 81-86, 2007.
[183] G. Graschew, T. A. Roelofs, S. Rakowsky, et al., "Telemedical applications and grid technology," in Lecture Notes in Computer Science, Amsterdam, 2005,vol. 3470, pp. 1-5.
[184] C. Del Frate, J. Galvez, T. Hauer, et al., "Final results and exploitation plans for MammoGrid," Studies in health technology and informatics, vol. 120,pp. 305-315, 2006.
[185] H. K. Huang, "Medical imaging informatics research and development trends - An editorial," Computerized Medical Imaging and Graphics, vol. 29, no. 2-3, pp. 91-93, 2005.
[186] H. K. Huang, Z. Aifeng, B. Liu, et al., "Data grid for large-scale medical image archive and analysis," in 13th Annual ACM International Conference on Multimedia, , Singapore, 2005, pp. 1005-1013.
[187] B. J. Liu, M. Z. Zhou, and J. Documet, "Utilizing data grid architecture for the backup and recovery of clinical image data," Computerized Medical Imaging and Graphics, vol. 29, no. 2-3, pp. 95-102, 2005.
[188] N. T. Karonis, B. Toonen, and I. Foster, "MPICH-G2: A grid-enabled implementation of the Message Passing Interface," Journal of Parallel and Distributed Computing, vol. 63, no. 5, pp. 551-563, 2003.
[189]王惠锋,孙正兴, and王箭, "语义图像检索研究进展,"计算机研究与发展, vol. 39, no. 5, pp. 513-523, 2002.
[2] H. Lu, X. Xue, and Y. P. Tan, "Content-based image and video indexing and retrieval," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4429 LNAI Shanghai, 2007, pp. 118-129.
[3] Part and K. K. Seo, "Content-based image retrieval by combining genetic algorithm and support vector machine," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4669 LNCS Porto, 2007, pp. 537-545.
[4] Part, J. Ha, and H. Choi, "The image retrieval method using multiple features," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4705 LNCS Kuala Lumpur, 2007, pp. 981-991.
[5] K. Wu and K. H. Yap, "Fuzzy SVM for content-based image retrieval," IEEE Computational Intelligence Magazine, vol. 1, no. 2, pp. 10-16, 2006.
[6] P. T. Yap and R. Paramesran, "Content-based image retrieval using Legendre chromaticity distribution moments," IEE Proceedings: Vision, Image and Signal Processing, vol. 153, no. 1, pp. 17-24, 2006.
[7] J. L. Bosque, O. D. Robles, L. Pastor, et al., "Parallel CBIR implementations with load balancing algorithms," Journal of Parallel and Distributed Computing, vol. 66, no. 8, pp. 1062-1075, 2006.
[8] S. H. Kwok and J. L. Zhao, "Content-based object organization for efficient image retrieval in image databases," Decision Support Systems, vol. 42, no. 3, pp. 1901-1916, 2006.
[9] M. Flickner, H. Sawhney, W. Niblack, et al., "Query by image and video content: the QBIC system," Computer, vol. 28, no. 9, pp. 23-32, 1995.
[10] W. Niblack, R. Barber, W. Equitz, et al., "QBIC project: querying images by content, using color, texture, and shape," in Proceedings of SPIE - The International Society for Optical Engineering, San Jose, CA, USA, 1993, vol. 1908, pp. 173-187.
[11] J. R. Bach, C. Fuller, A. Gupta, et al., "Virage image search engine: an open framework for image management," in Proceedings of SPIE - The International Society for Optical Engineering, San Jose, CA, USA, 1996, vol. 2670, pp. 76-87.
[12] Y. Rui, T. S. Huang, and S. Mehrotra, "Content-based image retrieval with relevance feedback in MARS," in IEEE International Conference on Image Processing, Santa Barbara, CA, USA, 1997, vol. 2, pp. 815-818.
[13] V. E. Ogle and M. Stonebraker, "Chabot: retrieval from a relational database of images," Computer, vol. 28, no. 9, pp. 40-48, 1995.
[14] S. D. MacArthur, C. E. Brodley, A. C. Kak, et al., "Interactive content-based image retrieval using relevance feedback," Computer Vision and Image Understanding, vol. 88, no. 2, pp. 55-75, 2002.
[15] B. C. Ko and H. Byun, "Query-by-gesture: An alternative content-based image retrieval query scheme," Journal of Visual Languages and Computing, vol. 13, no. 4, pp. 375-390, 2002.
[16] A. Pentland, R. W. Picard, and S. Sclaroff, "Photobook: Content-based manipulation of image databases," International Journal of Computer Vision, vol. 18, no. 3, pp. 233-254, 1996.
[17] W. Y. Ma and B. S. Manjunath, "NeTra: A toolbox for navigating large image databases," Multimedia Systems, vol. 7, no. 3, pp. 184-198, 1999.
[18]高原,耿国华,刘晓宁, "一种基于遗传算法自学习的图像检索方法,"西北大学学报(自然科学版), vol. 35, no. 04, pp. 379-382, 2005.
[19]赵莹, "基于颜色分布的图像检索系统,"微计算机信息, no. 18, 2007.
[20]杨振亚,杨振东,王勇,王成道, "基于色彩共生矩阵的CBIR方法,"计算机工程与应用, vol. 43, no. 20, pp. 40-42,51, 2007.
[21]邱开金,肖国强,张为群, "基于块边缘特征直方图的图像检索,"计算机科学, vol. 33, no. 04, pp. 215-217, 2006.
[22]唐波,孙茂印, "基于分块颜色相关向量的图像检索算法,"微计算机应用, vol. 28, no. 06, pp. 561-565, 2007.
[23] S.-K. Chang and T. Kunii, "Pictorial data-base applications," IEEE Comput. , vol. 14, no. 11, pp. 13-21, 1981.
[24] P. G. B. Enser, "Pictorial information retrieval," J. Document., vol. 51, no. 2, p. 126—170, 1995.
[25] A. Gupta and R. Jain, "Visual information retrieval," Communications of the ACM, vol. 40, no. 5, pp. 71-79, 1997.
[26] T. S. H. Y. Rui, S.-F. Chang, "Image retrieval: past, present and future," in the International Symposium on Multimedia Information Processing, Taipei, Taiwan, 1997.
[27] Y. Rui, T. S. Huang, and S. F. Chang, "Image retrieval: Current techniques, promising directions, and open issues," Journal of Visual Communication and Image Representation, vol. 10, no. 1, pp. 39-62, 1999.
[28] A. W. M. Smeulders, M. Worring, S. Santini, et al., "Content-based image retrieval at the end of the early years," IEEE Transactions on Pattern Analysisand Machine Intelligence, vol. 22, no. 12, pp. 1349-1380, 2000.
[29] K. K. Seo, "An application of one-class support vector machines in content-based image retrieval," Expert Systems with Applications, vol. 33, no. 2, pp. 491-498, 2007.
[30] M. J. Huiskes and E. J. Pauwels, "Indexing, Learning and Content-Based Retrieval for Special Purpose Image Databases," in Advances in Computers. vol. 65, M. Zelkowitz, Ed., 2005, pp. 203-258.
[31] S. C. Cheng, T. C. Chou, C. L. Yang, et al., "A semantic learning for content-based image retrieval using analytical hierarchy process," Expert Systems with Applications, vol. 28, no. 3, pp. 495-505, 2005.
[32] C. R. Shyu, C. E. Brodley, A. C. Kak, et al., "ASSERT: A physician-in-the-loop content-based retrieval system for HRCT image databases," Computer Vision and Image Understanding, vol. 75, no. 1, pp. 111-132, 1999.
[33] P. M. Kelly, T. M. Cannon, and J. E. Barros, "Efficiency issues related to probability density function comparison," in Proceedings of SPIE - The International Society for Optical Engineering, San Jose, CA, USA, 1996, vol. 2670, pp. 42-49.
[34] H. D. Tagare, F. M. Vos, C. C. Jaffe, et al., "Arrangement: a spatial relation between parts for evaluating similarity of tomographic section," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 9, pp. 880-893, 1995.
[35] J. Z. Wang, "Pathfinder: multiresolution region-based searching of pathology images using IRM," Proceedings / AMIA . Annual Symposium. AMIA Symposium, pp. 883-887, 2000.
[36] R. Panchal and B. Verma, "Characterization of breast abnormality patterns in digital mammograms using auto-associaitor neural network," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Hong Kong, 2006, vol. 4234 LNCS - III, pp. 127-136.
[37] W. Cai, D. Feng, and R. Fulton, "Content-based retrieval of dynamic PET functional images," IEEE Transactions on Information Technology in Biomedicine, vol. 4, no. 2, pp. 152-158, 2000.
[38] C. Y. Han, H. Chen, L. He, et al., "A Web-based distributed image processing system," in Proceedings of SPIE - The International Society for Optical Engineering, Santa Clara, CA, 2003, vol. 5018, pp. 111-122.
[39] T. M. Lehmann, M. O. Gu?ld, C. Thies, et al., "Content-based image retrieval in medical applications for picture archiving and communication systems," in Proceedings of SPIE - The International Society for Optical Engineering, San Diego, CA, 2003, vol. 5033, pp. 109-117.
[40] Y. Tan, J. Zhang, Y. Hua, et al., "Content-based image retrieval in Picture Archiving and Communication System," in Progress in Biomedical Optics and Imaging - Proceedings of SPIE, San Diego, CA, 2006, vol. 6145.
[41] D. Zhang, Y. Yang, and Q. Q. Qin, "A fast image retrieval algorithm with multi-channel textural features in PACS," Wuhan University Journal of Natural Sciences, vol. 10, no. 5, pp. 847-850, 2005.
[42] H. Greenspan and A. T. Pinhas, "Medical image categorization and retrieval for PACS using the GMM-KL framework," IEEE Transactions on Information Technology in Biomedicine, vol. 11, no. 2, pp. 190-202, 2007.
[43] S. Batty, A. Blandford, J. Clark, et al., "Content based retrieval of lesioned brain images," in Proceedings of SPIE - The International Society for Optical Engineering, San Diego, CA, 2002, vol. 4685, pp. 128-136.
[44] S. Atnafu, R. Chbeir, and L. Brunie, "Content-based and metadata retrieval in medical image database," in Proceedings of the IEEE Symposium on Computer-Based Medical Systems, Maribor, 2002, pp. 327-332.
[45] I. El-Naqa, Y. Yang, N. P. Galatsanos, et al., "Content-based image retrieval for digital mammography," in IEEE International Conference on Image Processing, Rochester, NY, 2002, vol. 3.
[46] S. Antani, L. R. Long, and G. R. Thoma, "Content-based image retrieval for large biomedical image archives," Medinfo, vol. 11, no. Pt 2, pp. 829-833, 2004.
[47] B. F. Jones, G. Schaefer, and S. Y. Zhu, "Content-based image retrieval for medical infrared images," in Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, San Francisco, CA, 2004, vol. 26 II, pp. 1186-1187.
[48] L. H. Y. Tang, R. Hanka, and H. H. S. Ip, "A review of intelligent content-based indexing and browsing of medical images," Health Informatics J., vol. 5,p. 40—49, 1999.
[49] S. S. Li and T. G. Zhuang, "Content-based medical image retrieval," Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation, vol. 25, no. 2, pp. 95-98, 2001.
[50] G. Scott and C. R. Shyu, "Knowledge-driven multidimensional indexing structure for biomedical media database retrieval," IEEE Transactions on Information Technology in Biomedicine, vol. 11, no. 3, pp. 320-331, 2007.
[51] T. Deselaers, H. Mu?ller, P. Clough, et al., "The CLEF 2005 automatic medical image annotation task," International Journal of Computer Vision, vol. 74, no. 1, pp. 51-58, 2007.
[52] S. Lefevre, J. Holler, and N. Vincent, "A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval," Real-Time Imaging, vol. 9, no. 1, pp. 73-98, 2003.
[53]周杰,陈武凡, "医学图像检索分类的MOAB编码方案,"中国图象图形学报, vol. 09, no. 02, pp. 214-219, 2004.
[54]王勇,吕扬生, "基于纹理特征的超声医学图像检索,"天津大学学报, vol. 38, no. 01, pp. 57-60, 2005.
[55]赵晨光,庄天戈, "基于内容的医学图像检索"国外医学.生物医学工程分册, no. 02期2004.
[56]温智韡,吴效明,郭圣文, "基于模糊聚类的医学图像检索方法,"中国医学物理学杂志, vol. 24, no. 03, pp. 180-183,223, 2007.
[57]董健卫,黄榕波,马建华, "多进制小波在医学图像检索中的应用,"医疗卫生装备, vol. 28, no. 01, pp. 5-7,10, 2007.
[58] H. Muller, N. Michoux, D. Bandon, et al., "A review of content-based image retrieval systems in medical applications - Clinical benefits and future directions," International Journal of Medical Informatics, vol. 73, no. 1, pp. 1-23, 2004.
[59] W. Bender, F. J. Paiz, W. Butera, et al., "Applications for data hiding," IBM Systems Journal, vol. 39, no. 3-4, pp. 547-568, 2000.
[60] W. Bender, D. Gruhl, N. Morimoto, et al., "Techniques for data hiding," IBM Systems Journal, vol. 35, no. 3-4, pp. 313-335, 1996.
[61] H. Trichili, M. S. Bouhlel, and L. Kamoun, "A review of watermarking techniques: Applications, properties, and domains," Journal of Testing and Evaluation, vol. 31, no. 4, pp. 357-360, 2003.
[62] R. Van Schyndel, A. Tirkel, and C. Osborne, "A digital watermark," IEEE International Conference on Image Processing, (Austin, Texas), November 1994, pp. 86-90, 2004.
[63] "Special issue on identification and protection of multimedia information," Proc. IEEE, vol. 87, no. 7, pp. 1059-1276, 1999.
[64] "Special Issue on Copyright and Privacy Protection," IEEE J. Select. Areas Commun., vol. 16,pp. 452-593, 1998.
[65] Z. Bojkovic and D. Milovanovic, "Multimedia contents security: Watermarking requirements and techniques," Recent Advances in Communications and Computer Science, pp. 178-183, 2003.
[66] A. M. Eskicioglu, "Multimedia security in group communications: Recent progress in key management, authentication, and watermarking," Multimedia Systems, vol. 9, no. 3, pp. 239-248, 2003.
[67] F. Hartung and M. Kutter, "Multimedia watermarking techniques," Proceedings of the IEEE, vol. 87, no. 7, pp. 1079-1107, 1999.
[68] H. S. Sung and S. W. Kim, "An improvement of auto-correlation based video watermarking scheme using perceptual masking for motion," in Lecture Notesin Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3816 LNCS, 2005, pp. 410-419.
[69] J. R. Hernandez and F. Perez-Gonzalez, "Statistical analysis of watermarking schemes for copyright protection of images," Proceedings of the IEEE, vol. 87, no. 7, pp. 1142-1166, 1999.
[70]陈峥,姚宇红,王晓京, "一种基于二维图形码的数字水印技术,"计算机应用, vol. 26, no. 08, pp. 1998-2000, 2006.
[71]刘瑞祯,谭铁牛, "数字图像水印研究综述,"通信学报, vol. 21, no. 08, pp. 39-48, 2000.
[72]谢勍, "数字水印在指纹识别中的应用研究,"微计算机应用, vol. 27, no. 01, pp. 98-100, 2006.
[73] X. Guo and T.-g. Zhuang, "A Region-Based Lossless Watermarking Scheme for Enhancing Security of Medical Data," Journal of Digital Imaging.
[74]程卫东,黄继武, and刘红梅, "一种基于3D-DCT的彩色图像信息隐藏算法,"自动化学报, no. 02, 2003.
[75]许君一,熊昌镇,齐东旭, et al., "量化水印算法分析,"通信学报, no. 03, 2006.
[76] J. Fridrich, M. Goljan, and R. Du, "Invertible authentication," in Proceedings of SPIE - The International Society for Optical Engineering, San Jose, CA, 2001, vol. 4314, pp. 197-208.
[77] M. Goljan, J. Friedrich, and R. Du, "Distortion-free data embedding for images," Fourth International Information Hiding Workshop, 2001.
[78] M. U. Celik, G. Sharma, A. M. Tekalp, et al., "Lossless generalized-LSB data embedding," IEEE Transactions on Image Processing, vol. 14, no. 2, pp. 253-266, 2005.
[79] S. K. Yip, O. C. Au, H. M. Wong, et al., "Generalized lossless data hiding by multiple predictors," in Proceedings - IEEE International Symposium on Circuits and Systems, Kos, 2006, pp. 1426-1429.
[80] G. Xuan, J. Zhu, J. Chen, et al., "Distortionless data hiding based on integer wavelet transform," Electronics Letters, vol. 38, no. 25, pp. 1646-1648, 2002.
[81] Z. Ni, Y. Q. Shi, N. Ansari, et al., "Reversible data hiding," in Proceedings - IEEE International Symposium on Circuits and Systems, Bangkok, 2003, vol. 2.
[82] B. Yang, M. Schmucker, W. Funk, et al., "Integer DCT-based reversible watermarking for images using companding technique," in Proceedings of SPIE - The International Society for Optical Engineering, San Jose, CA, 2004, vol. 5306, pp. 405-415.
[83] Y. Hu, B. Jeon, Z. Lin, et al., "Analysis and comparison of typical reversiblewatermarking methods," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4283 LNCS Jeju Island, 2006, pp. 333-347.
[84] D. Coltuc and A. Tremeau, "Simple reversible watermarking schemes," in Proceedings of SPIE - The International Society for Optical Engineering, San Jose, CA, 2005, vol. 5681, pp. 561-568.
[85] D. Coltuc and J. M. Chassery, "Simple reversible watermarking schemes: Further results," in Proceedings of SPIE - The International Society for Optical Engineering, San Jose, CA, 2006, vol. 6072.
[86] J. Tian, "Reversible Data Embedding Using a Difference Expansion," IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, no. 8, pp. 890-896, 2003.
[87] J. Tian, "Reversible watermarking by difference expansion," ACM Multimedia 2002 Workshop W2 - Workshop on Multimedia and Security: Authentication, Secrecy, and Steganalysis, pp. 19-22, 1 December 2002 through 6 December 2002 2002.
[88] A. M. Alattar, "Reversible watermark using difference expansion of triplets," in IEEE International Conference on Image Processing, Barcelona, 2003, vol. 1, pp. 501-504.
[89] A. M. Alattar, "Reversible watermark using the difference expansion of a generalized integer transform," IEEE Transactions on Image Processing, vol. 13, no. 8, pp. 1147-1156, 2004.
[90] L. Kamstra and H. J. A. M. Heijmans, "Reversible data embedding into images using wavelet techniques and sorting," IEEE Transactions on Image Processing, vol. 14, no. 12, pp. 2082-2090, 2005.
[91] D. M. Thodi and J. J. Rodri?guez, "Reversible watermarking by prediction-error expansion," in Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, Lake Tahoe, NV, 2004, vol. 6, pp. 21-25.
[92] D. M. Thodi and J. J. Rodriguez, "Expansion embedding techniques for reversible watermarking," IEEE Transactions on Image Processing, vol. 16, no. 3, pp. 721-730, 2007.
[93] R. A. U, D. Anand, S. B. P, et al., "Compact storage of medical images with patient information," IEEE Transactions on Information Technology in Biomedicine, vol. 5, no. 4, pp. 320-323, 2001.
[94] X. Kong and R. Feng, "Watermarking medical signals for telemedicine," IEEE Transactions on Information Technology in Biomedicine, vol. 5, no. 3, pp. 195-201, 2001.
[95] X. Q. Zhou, S. L. Lou, and H. K. Huang, "Authenticity and integrity of digital mammographic images," in Proceedings of SPIE - The International Societyfor Optical Engineering, San Diego, CA, USA, 1999, vol. 3662, pp. 138-144.
[96] H. M. Chao, C. M. Hsu, and S. G. Miaou, "A data-hiding technique with authentication, integration, and confidentiality for electronic patient records," IEEE Transactions on Information Technology in Biomedicine, vol. 6, no. 1, pp. 46-53, 2002.
[97] F. Bao, R. H. Deng, B. C. Ooi, et al., "Tailored reversible watermarking schemes for authentication of electronic clinical atlas," IEEE Transactions on Information Technology in Biomedicine, vol. 9, no. 4, pp. 554-563, 2005.
[98] M. Engin, O. C?idam, and E. Z. Engin, "Wavelet transformation based watermarking technique for human Electrocardiogram (ECG)," Journal of Medical Systems, vol. 29, no. 6, pp. 589-594, 2005.
[99] Z. Zhou, H. K. Huang, and B. J. Liu, "Digital Signature Embedding (DSE) for medical image integrity in a data grid off-site backup archive," in Progress in Biomedical Optics and Imaging - Proceedings of SPIE, San Diego, CA, 2005, vol. 5748, pp. 306-317.
[100]都志辉,陈渝,刘鹏,网格计算:清华大学出版社, 2002.
[101]金海,袁平鹏,石柯,网格计算(第二版):电子工业出版社, 2004.
[102] K. Czajkowski , I. Foster , and C. Kesselman "Co-allocation services for computational Grids," in In 8th IEEE Internatioanl Symposium on High Performance Distributed Computing, Los Alamitos, CA, , 1999.
[103] K. Anstreicher, N. Brixius, J. P. Goux, et al., "Solving large quadratic assignment problems on computational grids," Mathematical Programming, Series B, vol. 91, no. 3, pp. 563-588, 2002.
[104]孙培德,胡月仙, "网格计算的研究进展及应用前景,"计算机时代, no. 01, pp. 1-5, 2003.
[105] J. M. Brady, D. J. Gavaghan, A. C. Simpson, et al., "eDiamond: a grid-enabled federated database of annotated mammograms," in Grid Computing: Making the Global Infrastructure a Reality, F. Berman, G. C. Fox, and A. J. G. Hey, Eds., 2003, pp. 923-943.
[106] S. R. Amendolia, F. Estrella, W. Hassan, et al., "MammoGrid: a service oriented architecture based medical grid application," in Grid and Cooperative Computing - GCC 2004. Third International Conference Proceedings, Wuhan, China, 2004, pp. 939-942.
[107] A. Amir, S. Srinivasan, and A. Efrat, "Search the audio, browse the video - A generic paradigm for video collections," Eurasip Journal on Applied Signal Processing, vol. 2003, no. 2, pp. 209-222, 2003.
[108] C. T. Yang, Y. L. Kuo, and C. L. Lai, "Designing computing platform for BioGrid," International Journal of Computer Applications in Technology, vol. 22, no. 1, pp. 3-13, 2005.
[109] C. Stark, B. J. Breitkreutz, T. Reguly, et al., "BioGRID: a general repositoryfor interaction datasets," Nucleic acids research, no. 34, 2006.
[110] C. H. Huang and S. Rajasekaran, "BioGrid*- Bridging life science and information technology," in 2005 IEEE International Symposium on Cluster Computing and the Grid, CCGrid 2005, Cardiff, Wales, 2005, vol. 1, pp. 450-454.
[111] C. H. Huang, V. Lanza, S. Rajasekaran, et al., "HealthGrid - Bridging life science and information technology," Journal of Clinical Monitoring and Computing, vol. 19, no. 4-5, pp. 259-262, 2005.
[112] V. Breton, K. Dean, T. Solomonides, et al., "The Healthgrid White Paper," Studies in health technology and informatics, vol. 112,pp. 249-321, 2005.
[113] I. C. Oliveira, J. L. Oliveira, J. P. Sanchez, et al., "Grid requirements for the integration of biomedical information resources for health applications," Methods of Information in Medicine, vol. 44, no. 2, pp. 161-167, 2005.
[114] Z. Yang and C. C. J. Kuo, "Survey on image content analysis, indexing, and retrieval techniques and status report of MPEG-7," Tamkang Journal of Science and Engineering, vol. 2, no. 3, pp. 101-118, 1999.
[115] B. M. Mehtre, M. S. Kankanhalli, and W. F. Lee, "Shape measures for content based image retrieval: A comparison," Information Processing and Management, vol. 33, no. 3, pp. 319-337, 1997.
[116] G. Mountrakis, A. Stefanidis, I. Schlaislch, et al., "Supporting quality-based image retrieval through user preference learning," Photogrammetric Engineering and Remote Sensing, vol. 70, no. 8, pp. 973-981, 2004.
[117] S. C. Horii, B. A. Levine, G. Goger, et al., "A comparison of case retrieval times: film versus picture archiving and communications systems," Journal of digital imaging : the official journal of the Society for Computer Applications in Radiology, vol. 5, no. 3, pp. 138-143, 1992.
[118] R. Malladi, J. A. Sethian, and B. C. Vemuri, "Shape modeling with front propagation: a level set approach," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 2, pp. 158-175, 1995.
[119] M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: Active contour models," International Journal of Computer Vision, vol. 1, no. 4, pp. 321-331, 1988.
[120] L. Vincent and P. Soille, "Watersheds in digital spaces: An efficient algorithm based on immersion simulations," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 6, pp. 583-598, 1991.
[121]赵晨光, "基于内容的肝脏CT图像检索的研究,"上海交通大学生物医学工程, 2005
[122] C. Brodley, A. Kak, C. Shyu, et al., "Content-based retrieval from medical image databases: a synergy of human interaction, machine learning and computer vision," Proceedings of the National Conference on Artificial Intelligence, pp. 760-767, 18 July 1999 through 22 July 1999 1999.
[123] W. W. Chu, "KMeD: A knowledge-based multimedia medical distributed database system," Information Systems, vol. 20, no. 2, pp. 75-96, 1995.
[124] B. S. Manjunath, "Gabor wavelet transform and application to problems in computer vision," in 26th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, 1992, pp. 796-800.
[125] J. Z. Wang, J. Li, and G. Wiederhold, "SIMPLIcity: Semantics-sensitive integrated Matching for Picture Libraries," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 9, pp. 947-963, 2001.
[126] A. K. Jain and A. Vailaya, "Image retrieval using color and shape," Pattern Recognition, vol. 29, no. 8, pp. 1233-1244, 1996.
[127] R. C. Gonzalez and R. E. Woods, "Segmentation by Morphological Watersheds," in Digital image Processing (Second Edition), 2002, pp. 617-621.
[128] L. Najman and M. Schmitt, "Geodesic saliency of watershed contours and hierarchical segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 12, pp. 1163-1173, 1996.
[129] F. Meyer, "An overview of morphological segmentation," International Journal of Pattern Recognition and Artificial Intelligence, vol. 15, no. 7, pp. 1089-1118, 2001.
[130]赵晨光,宋利伟, and庄天戈, "基于分水岭区域差异性的多级图像分割,"上海交通大学学报, vol. 38, no. 9, pp. 1489-1491, 2004.
[131] A. Mehnert and P. Jackway, "An improved seeded region growing algorithm," Pattern Recognition Letters, vol. 18, no. 10, pp. 1065-1071, 1997.
[132] A. Bleau and L. J. Leon, "Watershed-based segmentation and region merging," Computer Vision and Image Understanding, vol. 77, no. 3, pp. 317-370, 2000.
[133] R. Lotufo and W. Silva, "Minimal Set of Markers for the Watershed Transform," Proceedings of International Symposium on mathematical morphology (ISMM2002), 2000.
[134] M. Wei, Y. Zhou, and M. Wan, "A fast snake model based on non-linear diffusion for medical image segmentation," Computerized Medical Imaging and Graphics, vol. 28, no. 3, pp. 109-117, 2004.
[135] X. M. Pardo, M. J. Carreira, A. Mosquera, et al., "A snake for CT image segmentation integrating region and edge information," Image and Vision Computing, vol. 19, no. 7, pp. 461-475, 2001.
[136] J. M. Pardo, D. Cabello, and J. Heras, "A snake for model-based segmentation of biomedical images," Pattern Recognition Letters, vol. 18, no. 14, pp. 1529-1538, 1997.
[137] C. Xu and J. L. Prince, "Snakes, shapes, and gradient vector flow," IEEE Transactions on Image Processing, vol. 7, no. 3, pp. 359-369, 1998.
[138] S. Suthaharan, "Image and edge detail detection algorithm for object-basedcoding," Pattern Recognition Letters, vol. 21, no. 6-7, pp. 549-557, 2000.
[139] H. Tang, E. X. Wu, Q. Y. Ma, et al., "MRI brain image segmentation by multi-resolution edge detection and region selection," Computerized Medical Imaging and Graphics, vol. 24, no. 6, pp. 349-357, 2000.
[140] S. Sanjay-Gopal and T. J. Hebert, "Bayesian pixel classification using spatially variant finite mixtures and the generalized EM algorithm," IEEE Transactions on Image Processing, vol. 7, no. 7, pp. 1014-1028, 1998.
[141] S. L. Sclove, "APPLICATION OF THE CONDITIONAL POPULATION-MIXTURE MODEL TO IMAGE SEGMENTATION," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-5, no. 4, pp. 428-433, 1983.
[142] D. M. Titterington, A. F. M. Smith, and U. E. Makov, "Statistical Analysis of Finite Mixture Distributions," New York: John Wiley., 1985.
[143] M. Porat and Y. Y. Zeevi, "Localized texture processing in vision: analysis and synthesis in the Gaborian space," IEEE Transactions on Biomedical Engineering, vol. 36, no. 1, pp. 115-129, 1989.
[144] M. Porat and Y. Y. Zeevi, "The Generalized Gabor Scheme Of Image Representation In Biological And Machine Vision," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, no. 4, pp. 452-468, 1988.
[145] A. K. Jain and F. Farrokhnia, "Unsupervised texture segmentation using Gabor filters," Pattern Recognition Letters, vol. 24, no. 12, pp. 1167-1186, 1991.
[146] D. Dunn and W. E. Higgins, "Optimal Gabor filters for Texture Segmentation," IEEE Transactions on Image Processing, vol. 4, no. 7, pp. 947-964, 1995.
[147] D. Dunn, W. E. Higgins, and J. Wakeley, "Texture segmentation using 2-D Gabor elementary functions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 2, pp. 130-149, 1994.
[148] D. Gabor, "Theory of communication," Inst. Elec. Eng., vol. 93,pp. 429-459, 1946.
[149] B. S. Manjunath and W. Y. Ma, "Texture features for browsing and retrieval of image data," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 837-842, 1996.
[150] J. G. Daugman, "Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters.," J. Opt. Soc Am. A pp. 1160-1169, 1985.
[151] J. R. Movellan, "tutorial on gabor filters," 1996.
[152] D. A. Pollen and S. F. Ronner, "Visual cortical neurons as localized spatial frequency filters," IEEE Transactions on Systems, Man and Cybernetics, vol. 13, no. 5, pp. 907-916, 1983.
[153] B. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers," in Proceedings of the Fifth Annual ACM Workshop onComputational Learning Theory, Pittsburgh, PA, USA, 1992, pp. 144-152.
[154] C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
[155] V. Vapnik, The nature of statistical learning theory. New York: Springer-Verlag, 1995.
[156] C.-C. Chang and C.-J. Lin, "LIBSVM : a library for support vector machines," 2001.
[157] C. Zhao, H. Cheng, and T. Zhuang, "Hepatic CT image query using Gabor features," Chinese Optics Letters, vol. 2, no. 7, pp. 383-385, 2004.
[158] A. Giakoumaki, S. Pavlopoulos, and D. Koutsouris, "Multiple image watermarking applied to health information management," IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 4, pp. 722-732, 2006.
[159] C. W. Honsinger, P. W. Jones, M. Rabbani, et al., "Lossless recovery of an original image containing embedded data," 2001.
[160] B. Macq, "Lossless multiresolution transform for image authenticating watermarking," Proc. EUSIPCO, Tampere, Finland, September 2000, 2000.
[161] J. Fridrich, M. Goljan, and R. Du, "Lossless data embedding for all image formats," in Proceedings of SPIE - The International Society for Optical Engineering, San Jose, CA, 2002, vol. 4675, pp. 572-583.
[162] T. Kalker and F. M. J. Willems, "Capacity bounds and constructions for reversible data-hiding," in Proceedings of SPIE - The International Society for Optical Engineering, Santa Clara, CA, 2003, vol. 5020, pp. 604-611.
[163] R. L. Rivest, "The MD4 message digest algorithm," RFC, vol. 1186,1990.
[164] R. Rivest, "The MD5 message-digest algorithm," RFC, vol. 1321,1992.
[165] R. L. Rivest, A. Shamir, and L. Adleman, "method for obtaining digital signatures and public-key cryptosystems," Communications of the ACM, vol. 21, no. 2, pp. 120-126, 1978.
[166] I. Foster, C. Kesselman, and S. Tuecke, "The anatomy of the grid: Enabling scalable virtual organizations," International Journal of High Performance Computing Applications, vol. 15, no. 3, pp. 200-222, 2001.
[167] E. Mangonaux, X. S. Zhou, and D. Comaniciu, "A comparative study of dissimilarity measures for histogram-based tracking in echocardiography," in Proceedings of SPIE - The International Society for Optical Engineering, San Diego, CA, 2004, vol. 5370 III, pp. 1509-1518.
[168] A. Parasyn, R. M. Hanson, J. K. Peat, et al., "A comparison between digital images viewed on a picture archiving and communication system diagnostic workstation and on a PC-based remote viewing system by emergency physicians," Journal of Digital Imaging, vol. 11, no. 1, pp. 45-49, 1998.
[169] B. Allcock, J. Bester, J. Bresnahan, et al., "Data management and transfer in high-performance computational grid environments," Parallel Computing, vol.28, no. 5, pp. 749-771, 2002.
[170]章鲁,李三立, "生物医学网格及其应用,"上海第二医科大学学报, vol. 25, no. 11, pp. 1177-1183, 2005.
[171] H. Duque, J. Montagnat, J. M. Pierson, et al., "DM2 : A distributed medical data manager for grids," in Ccgrid 2003: 3rd Ieee/Acm International Symposium on Cluster Computing and the Grid, Proceedings, F. Titsworth and D. Azada, Eds., 2003, pp. 606-611.
[172] K. Hassan, T. Tweed, and S. Miguet, "A multi-resolution approach for content-based image retrieval on the grid: Application to breast cancer detection," Methods of Information in Medicine, vol. 44, no. 2, pp. 211-214, 2005.
[173] D. Slik, O. Seiler, T. Altman, et al., "Scalable fault tolerant image communication and storage grid," in Proceedings of SPIE - The International Society for Optical Engineering, San Diego, CA, 2003, vol. 5033, pp. 36-47.
[174] X. Zhang and J. Zhang, "Data grid - A distributed solution to PACS," in Progress in Biomedical Optics and Imaging, San Diego, CA, 2004, vol. 5, pp. 334-344.
[175] "The OGSA-DAI project," in http://www.ogsa-dai.org.uk/ 2005.
[176] I. Kojima and S. M. Pahlevi, "Design and implementation of OGSA-WebDB," in http://www.nesc.ac.uk/events/GGF10-DA/ 2004.
[177] "OGSA-DQP," in http://www.ogsa-dai.org.uk/dqp/ 2004.
[178] E. Bagarinao, L. Sarmenta, Y. Tanaka, et al., "The Application of Grid Computing to Real-Time Functional MRI Analysis," in Parallel and Distributed Processing and Applications, 2004, pp. 290-302.
[179] T. C. Pan, M. N. Gurcan, S. A. Langella, et al., "Informatics in radiology: GridCAD: grid-based computer-aided detection system," Radiographics : a review publication of the Radiological Society of North America, Inc, vol. 27, no. 3, pp. 889-897, 2007.
[180] M. C. Oliveira, W. Cirne, and P. M. de Azevedo Marques, "Towards applying content-based image retrieval in the clinical routine," Future Generation Computer Systems, vol. 23, no. 3, pp. 466-474, 2007.
[181] M. Redmond, E. Brodsky, Y. H. Hu, et al., "The 4D cluster visualization project," in Proceedings of SPIE - The International Society for Optical Engineering, San Diego, CA, 2004, vol. 5367, pp. 28-38.
[182] H. Pohjonen, P. Ross, J. G. Blickman, et al., "Pervasive access to images and data - The use of computing grids and mobile/wireless devices across healthcare enterprises," IEEE Transactions on Information Technology in Biomedicine, vol. 11, no. 1, pp. 81-86, 2007.
[183] G. Graschew, T. A. Roelofs, S. Rakowsky, et al., "Telemedical applications and grid technology," in Lecture Notes in Computer Science, Amsterdam, 2005,vol. 3470, pp. 1-5.
[184] C. Del Frate, J. Galvez, T. Hauer, et al., "Final results and exploitation plans for MammoGrid," Studies in health technology and informatics, vol. 120,pp. 305-315, 2006.
[185] H. K. Huang, "Medical imaging informatics research and development trends - An editorial," Computerized Medical Imaging and Graphics, vol. 29, no. 2-3, pp. 91-93, 2005.
[186] H. K. Huang, Z. Aifeng, B. Liu, et al., "Data grid for large-scale medical image archive and analysis," in 13th Annual ACM International Conference on Multimedia, , Singapore, 2005, pp. 1005-1013.
[187] B. J. Liu, M. Z. Zhou, and J. Documet, "Utilizing data grid architecture for the backup and recovery of clinical image data," Computerized Medical Imaging and Graphics, vol. 29, no. 2-3, pp. 95-102, 2005.
[188] N. T. Karonis, B. Toonen, and I. Foster, "MPICH-G2: A grid-enabled implementation of the Message Passing Interface," Journal of Parallel and Distributed Computing, vol. 63, no. 5, pp. 551-563, 2003.
[189]王惠锋,孙正兴, and王箭, "语义图像检索研究进展,"计算机研究与发展, vol. 39, no. 5, pp. 513-523, 2002.