虹膜识别技术在医疗信息管理系统中的设计与实现
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摘要
虹膜识别技术是一种以人体虹膜生理特征为依据的生物特征识别技术,与声音、脸像、掌纹、指纹等特征识别相比,虹膜具有稳定性、唯一性、非侵犯性、识别率高等优点,因此虹膜识别技术已成为目前重要的身份识别研究领域之一。本文主要针对虹膜识别技术的一些关键问题及其在医疗信息管理系统中的应用进行研究。
首先根据虹膜识别各阶段的划分,对各阶段的算法进行了深入的分析和比较。在虹膜采集阶段,对利用虹膜采集仪获取的虹膜图像实现了滤波处理,达到了降低后续操作数据量的效果;在虹膜定位阶段实现了将canny算子与灰度投影法相结合较好地完成了对虹膜图像内外边界的定位;对定位好的虹膜图像,实现以极坐标变换的方法完成归一化并通过灰度直方图均衡化法对归一化后的图像进行增强;在特征提取阶段完成了用2D Gobar滤波算法对虹膜特征值进行提取并编码;最后实现了用海明距离算法对编码后的虹膜图像进行模式匹配。
其次在深入研究医疗信息管理的应用现状,明确了医疗信息管理系统所面临的安全性难题后,开发了应用虹膜识别技术的医疗信息管理系统。本系统实现了以虹膜特征为依据的用户身份认证,病历信息管理,用户管理和系统管理的功能。通过对系统各模块的具体实现、系统集成、集成测试及测验,证明本课题所研究的内容是可行的。
Iris recognition technology is a kind of biometric identification technology, based on human iris physiological characteristics. The iris has many advantages, such as the stability, uniqueness, non-invasive and the recognition rate, compared with the features recognition of voice, face image, palm print, fingerprint, so iris recognition technology has become one of the important research areas of identification. This paper is to study some of the key issues of iris recognition technology and its application in health information management system.
Based on these divisions of all stages in iris recognition these algorithms of various stages are analyzed and compared. In the iris acquisition stage, iris image achieved by acquisition instrument is filtered, which reduces the amount of data. In the iris location stage, the canny operator and gray projection are combined to locate the inside and outside the boundaries of the iris image. If the iris image has been located well, it can be used the polar coordinate transformation method to complete normalization. And the normalized iris image can be enhanced by the histogram equalization method. In the feature extraction stage the iris feature value is extracted and encoded by the 2D Gabor filtering algorithm. Finally it is realized that the iris image is pattern matched by Hamming distance algorithm.
Studing the status of medical information management application depthly, the security problems faced by medical information management system clearly. Then the medical information management system applicating iris recognition technology is developed. This system achieve these system management functions, including the user authentication based on the iris characteristics, the medical records management, the user management and the system management. The specific implementation of each module, system integration, integration testing and test prove the content of the topic under study is feasible. It improves the effectiveness and security of medical information management.
首先根据虹膜识别各阶段的划分,对各阶段的算法进行了深入的分析和比较。在虹膜采集阶段,对利用虹膜采集仪获取的虹膜图像实现了滤波处理,达到了降低后续操作数据量的效果;在虹膜定位阶段实现了将canny算子与灰度投影法相结合较好地完成了对虹膜图像内外边界的定位;对定位好的虹膜图像,实现以极坐标变换的方法完成归一化并通过灰度直方图均衡化法对归一化后的图像进行增强;在特征提取阶段完成了用2D Gobar滤波算法对虹膜特征值进行提取并编码;最后实现了用海明距离算法对编码后的虹膜图像进行模式匹配。
其次在深入研究医疗信息管理的应用现状,明确了医疗信息管理系统所面临的安全性难题后,开发了应用虹膜识别技术的医疗信息管理系统。本系统实现了以虹膜特征为依据的用户身份认证,病历信息管理,用户管理和系统管理的功能。通过对系统各模块的具体实现、系统集成、集成测试及测验,证明本课题所研究的内容是可行的。
Iris recognition technology is a kind of biometric identification technology, based on human iris physiological characteristics. The iris has many advantages, such as the stability, uniqueness, non-invasive and the recognition rate, compared with the features recognition of voice, face image, palm print, fingerprint, so iris recognition technology has become one of the important research areas of identification. This paper is to study some of the key issues of iris recognition technology and its application in health information management system.
Based on these divisions of all stages in iris recognition these algorithms of various stages are analyzed and compared. In the iris acquisition stage, iris image achieved by acquisition instrument is filtered, which reduces the amount of data. In the iris location stage, the canny operator and gray projection are combined to locate the inside and outside the boundaries of the iris image. If the iris image has been located well, it can be used the polar coordinate transformation method to complete normalization. And the normalized iris image can be enhanced by the histogram equalization method. In the feature extraction stage the iris feature value is extracted and encoded by the 2D Gabor filtering algorithm. Finally it is realized that the iris image is pattern matched by Hamming distance algorithm.
Studing the status of medical information management application depthly, the security problems faced by medical information management system clearly. Then the medical information management system applicating iris recognition technology is developed. This system achieve these system management functions, including the user authentication based on the iris characteristics, the medical records management, the user management and the system management. The specific implementation of each module, system integration, integration testing and test prove the content of the topic under study is feasible. It improves the effectiveness and security of medical information management.
引文
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[34]白春艳. Wildes虹膜识别方法的研究.长春师范学院学报(自然科学版), 2009, 02: 20-23
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[38]黄惠芳,胡广书.虹膜识别算法的研究及实现.红外与激光工程, 2002, 31(5): 404-409
[39]康浩,徐国治.虹膜纹理的能量编码.电路与系统学报, 2003, 5(1): 444-492
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[2] J. DAUGMAN. High Confidence Personal Identification by Rapid Video Analysis of Iris Texture. The IEEE 1992 International Carnahan Conference on Security Technology, Atlanta, USA, 1992: 50-60
[3] R. P. WILDES. Iris Recognition:an Emerging Biometric Technology. Proceeding of IEEE, 1997, 85(9): 1348-1363
[4] W. W. BOLES, B. BOASHASH. A Human Identification Technique Using Images of The Iris and Wavelet Transform, IEEE Transations on Signal Processing, 1998, 46(4): 1185-1188
[5]王蕴红,朱永,谭铁牛.基于虹膜识别的身份鉴别.自动化学报, 2002, 28(1): 1-10
[6]叶学义,庄镇泉.虹膜识别系统.闭电路与系统学报, 2003, 8(3): 75-80
[7]魏连鑫.多小波理论及其在虹膜识别技术中的应用. [吉林大学博士论文]. 2006: 4-5
[8] J. G. DAUGMAN. High Confidence Visual Recognition of Persons by A Test of Statistical Independence. IEEE Tractions on Pattern Analysis and Machine Intelligence, 1993, 15(11): 1148-1161
[9]刘志成,林丁.虹膜组织力学特性研究的实验方法探索.中国医学物理学杂志, 2001, 18(4): 234-248
[10] J. G. DANGNMAN. How Iris Recognition Works. IEEE Transactions on Circuits and Systems for Video Technology, 2004, 14(1): 21-30
[11]康浩,徐国治.虹膜纹理的相位编码.上海交通大学学报, 1999, 33(5): 542-544
[12]刘丽华.人脸识别方法综述.云南师范大学学报, 2005, 25(26): 4-19
[13] H.PROENCA, L.A.ALEXANDRE. Iris Segmentation Methodology for Non-cooperative Recognition. IEEE Proc. Vis. Image Signal Process, 2006, 153(2): 653-656
[14]应忍冬,徐国治.基于小波过零检测的虹膜识别技术.上海交通大学学报, 2002, 3: 542-544
[15]苑玮琦,徐露,林忠华.一种基于人眼图像灰度分布特征的虹膜定位算法.光电子激光, 2006, 17(2): 226-230
[16]严民军,汪云九.虹膜识别的计算机识别原理.生物化学与生物物理进展, 2000, 27(4): 348-350
[17] W. W. BOLES, B. A. BOASHAH. Human Identification Technique Using Image of The Iris and Wavelet Transform. IEEE Trans. On Signal Processing, 1998, 46(4): 1185-1188
[18]杨惠玲,潘景轩,吴伟康.高级病理生理学.北京:科学出版社, 2006
[19] J. DAUGMAN, Demodulation by Complex-Valued Wavelets for Stochastic Pattern Recognition.Multiresolution and Information Processing, 2003, 1(1): 1-17
[20]周俊.罗挺,王帅,等.不均匀光照下的虹膜定位算法研究.后勤工程学院学报, 2010, 6: 78-86
[21]周啸,沈建新,廖文和,等.基于区域灰度变化的虹膜识别,光电工程, 2010, 10: 104-110
[22]林邓伟,夏美云.虹膜特征点的提取分析与研究.微计算机信息, 2007, 07: 293-295
[23]卢光明,戚佳音,廖庆敏.一种虹膜图像质量评价的方法.计算机工程与应用, 2007, 26: 197-199
[24]李东晓.神奇的生物特征识别技术.人民公安, 2007, 19: 52-53
[25]吴慧敏,郑晓势,赵彦玲.虹膜识别技术综述.大众科技, 2008, 01: 41-43
[26]李六争,黄健,崔丽.几种虹膜识别算法的比较.大连海事大学学报, 2008, 4: 153-155
[27]岳学东,刘洋.一种新的基于完整相位分析的虹膜分类算法.计算机工程与应用, 2008, 23: 201-202
[28]尹德森.生物特征识别设备综述.警察技术, 2010, 05: 50-53
[29]宗胜强,刘振宇,王清. VC环境下数据库ADO开发技术的应用.河北建筑工程学院学报, 2007(3): 94-96
[30]陈爱萍,张可为.基于2D-Gabor滤波器的虹膜识别算法实现.计算机与数字工程, 2010, 06: 153-157
[31]叶永强,沈建新,周啸基.于瞳孔灰度特征的快速虹膜定位.光电工程, 2010, 03: 127-132
[32]陈广华,何希勤,刘向东.一种有效的虹膜定位及睫毛检测方法.计算机工程与应用, 2010, 06: 171-173
[33]郑煊,王强德,刘萌.基于虹膜图像特征的噪声检测方法.光电子技术, 2009, 01: 37-41
[34]白春艳. Wildes虹膜识别方法的研究.长春师范学院学报(自然科学版), 2009, 02: 20-23
[35]苑玮琦,张志文.基于小波过零点检测的虹膜识别方法研究.计算机应用研究, 2008, 09: 45-47
[36]应忍冬,徐国治.基于小波变换过零检测的虹膜识别技术.上海交通大学学报, 2002,36(3): 354-358
[37]李庆嵘.虹膜定位算法研究.电子科技大学学报, 2002, 31(1): 7-9
[38]黄惠芳,胡广书.虹膜识别算法的研究及实现.红外与激光工程, 2002, 31(5): 404-409
[39]康浩,徐国治.虹膜纹理的能量编码.电路与系统学报, 2003, 5(1): 444-492
[40]阮秋琦.数字图像处理学.北京:电子工业出版社, 2001
[41]夏良正.数字图像处理(修订版).南京:东南大学出版社, 1999
[42]勒济芳. Visua1C++小波变换技术与工程实践.北京:人民邮电出版社, 2004
[43]贾云德.机器视觉.北京:科学出版社, 2000: 97-100
[44]路锦正,周冬梅. Visual C++音频/视频处理技术及工程实践,北京:电子工业出版社, 2009
[45]求是科技. Visual C++数字图像处理典型算法及实现.北京:人民邮电出版社, 2006
[46] D. J. KRUGLINSKI. Visual C++技术内幕.北京:清华大学出版社, 1999
[47]侯俊杰.深入浅出MFC.第2版.武汉:华中科技大学出版社, 2001
[48]陈兵期,孙明. Visual C++实用图像处理专业教程.北京:清华大学出版社, 2004
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