视网膜图像处理与眼科在线网站系统的开发研究
|
摘要
随着可视化技术的不断发展,现代医学已经越来越离不开医学影像的信息处理,通过图像图形技术,使得医务工作者可以从多方位多层次的观察角度对影像数据进行详细的观察,可以辅助医生对病变体及其他感兴趣的区域进行定性直至准确的定量分析,而且可以大大提高临床诊断的准确性和正确性。图像处理技术引入眼科已近10年,其应用也日趋广泛,眼底图像的计算机分析系统的建立,可以对眼底许多组织进行定量测量,在正常和异常之间提供鉴别帮助,使眼底系统形态的研究向标准化和功能分析进展,从而大大加强眼底检查优势。
课题针对视网膜图像的特点和眼科的实际需求,从软件开发设计的角度,根据RIS系统(Radiology Information System)的开发规范。一方面用VC++编程工具针对性的选取一些图像处理方法完成视网膜图像处理系统模块;另一方面建立眼科在线网站系统,提供无纸化医疗过程,实现数字化管理。与此同时,将病人的视网膜图像和信息存储在数据库中,便于管理。在视网膜图像处理系统模块将视网膜图像处理方法分为图像增强、图像几何变换、图像分割、图像配准与拼接、血管管径测量、彩色图像处理等。这些处理方法能够较为全面的给医生提供分析工具,帮助医生更好的分析患者病情并做出诊断。
在眼科在线网站系统中,实现了无纸化管理,能够通过网站系统登记患者的档案,对患者档案、患者图像、留言等进行管理。系统具有较好的再开发性和可拓展性,使用方便。
Along with the technology of Visualization development, the modern medicine already more and more could not leave the medicine phantom the information processing, through the image graph technology, causes the medical worker to be possible to carry on the detailed observation from the multi-positions multi-level observation angle to the phantom data, may assist doctor and other is interested to sickness anomalous form the region qualitatively to carry on until the accurate quantitative analysis, moreover may enhance the clinical diagnosis greatly accuracy. Imagery processing technology introduction ophthalmology department already nearly 10 years, its application also widespread, the eyeground image computer analysis system establishment, may carry on the quantitative measurement to eyeground many organizations, normal and exceptionally provides the distinction help, causes the eyeground system shape the research progresses to the standardization and the functional analysis, thus strengthens the eyeground inspection superiority greatly.
Topic in retina image characteristic and ophthalmology department's actual demanded, from software development design angle, according to RIS system development standard. On the one hand, programs pointed selection some imagery processing methods with VC++ to complete the retina imagery processing system module; On the other hand, establishment ophthalmology department on-line website system, provides Scripless the medical procedure, the realization digitization management. At the same time, patient's retina image and the information storage in the database, is advantageous for the management. Dividing into the retina imagery processing system, the retina imagery processing method includes enhancement, geometry transformation, division, matching and mosaic, the blood vessel caliber survey, color image processing and so on.. These processing methods can comprehensive provide to doctor analyze tool, help doctor the better analysis patient condition and make the diagnosis.
The ophthalmology on-line website system, has realized scripless management, can register patient’s docments, the patient’s images, the message and so on. The system has good the development, easy operate.
课题针对视网膜图像的特点和眼科的实际需求,从软件开发设计的角度,根据RIS系统(Radiology Information System)的开发规范。一方面用VC++编程工具针对性的选取一些图像处理方法完成视网膜图像处理系统模块;另一方面建立眼科在线网站系统,提供无纸化医疗过程,实现数字化管理。与此同时,将病人的视网膜图像和信息存储在数据库中,便于管理。在视网膜图像处理系统模块将视网膜图像处理方法分为图像增强、图像几何变换、图像分割、图像配准与拼接、血管管径测量、彩色图像处理等。这些处理方法能够较为全面的给医生提供分析工具,帮助医生更好的分析患者病情并做出诊断。
在眼科在线网站系统中,实现了无纸化管理,能够通过网站系统登记患者的档案,对患者档案、患者图像、留言等进行管理。系统具有较好的再开发性和可拓展性,使用方便。
Along with the technology of Visualization development, the modern medicine already more and more could not leave the medicine phantom the information processing, through the image graph technology, causes the medical worker to be possible to carry on the detailed observation from the multi-positions multi-level observation angle to the phantom data, may assist doctor and other is interested to sickness anomalous form the region qualitatively to carry on until the accurate quantitative analysis, moreover may enhance the clinical diagnosis greatly accuracy. Imagery processing technology introduction ophthalmology department already nearly 10 years, its application also widespread, the eyeground image computer analysis system establishment, may carry on the quantitative measurement to eyeground many organizations, normal and exceptionally provides the distinction help, causes the eyeground system shape the research progresses to the standardization and the functional analysis, thus strengthens the eyeground inspection superiority greatly.
Topic in retina image characteristic and ophthalmology department's actual demanded, from software development design angle, according to RIS system development standard. On the one hand, programs pointed selection some imagery processing methods with VC++ to complete the retina imagery processing system module; On the other hand, establishment ophthalmology department on-line website system, provides Scripless the medical procedure, the realization digitization management. At the same time, patient's retina image and the information storage in the database, is advantageous for the management. Dividing into the retina imagery processing system, the retina imagery processing method includes enhancement, geometry transformation, division, matching and mosaic, the blood vessel caliber survey, color image processing and so on.. These processing methods can comprehensive provide to doctor analyze tool, help doctor the better analysis patient condition and make the diagnosis.
The ophthalmology on-line website system, has realized scripless management, can register patient’s docments, the patient’s images, the message and so on. The system has good the development, easy operate.
引文
【1】 田捷,包尚联,周明全.医学影像处理于分析[M]. 北京:电子工业出版社,2003.1
【2】 杨钧主编.现代眼科手册(第二版)[M].人民卫生出版社,1996
【3】 中山医学院主编.眼科学[J],人民卫生出版社,1980
【4】 http://www.oculist.net/Article/ShowNew.asp?page=40
【5】 http://hbg.hongbage.net/zhenshi/index.php?zhenshiid=17499
【6】 http://www.eye120.cn/
【7】 A.Can,H.Shen,J.N.Turner,H.L.Tanenbaum,d B.Roysam. Rapid automated tracing and feature extraction from live high-resolution retinal fundus images using direct exploratory algorithms [J]. IEEE Trans. on Info. Tech. for Biomedicine, 1999,3(2): 125–138.
【8】 Y.A.Tolias, S.M.Panas. A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering [J]. IEEE Trans. Med. Image.,1998,17(2):263-273.
【9】 S. Chaudhuri,S. Chatterjee,N. Katz,M. Nelson,M. Goldbaum. Detection of blood vessels in retinal images using two- dimensional matched filters [J]. IEEE Trans. Med. Image.,1989,8 (3): 263–269.
【10】 Chutatape,L.Zheng,S. Krishman. Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters[C]. Proceeding of IEEE Int Conf Emg and Bio Society,1998,20:3144–3149.
【11】 X.Jiang,D.Mojon. Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images [J]. IEEE Trans. Pattern Anal. Machine Intell. 2003,25(1):131–137.
【12】 A.Hoover,V.Kouznetsova,M.Goldmaum. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter respone[J]. IEEE Trans Med Imaging,2000,19(3):203-210.
【13】 T.McInerney,D.Terzopoulos. T-snakes: Topology adaptive snakes [J]. Med. Imag. Anal.,2000,4 (2): 73–91.
【14】 F.Zana,J.C.Klein. Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation [J]. IEEE Trans Image Processing, 2001, 10(7):1010–1019.
【15】 J. chanussot,P. lambert. An application of mathematical morphology to road network extraction on SAR images [J]. Mathematic Morphology and Its Application to image and signal Processing. 1998:399-406.
【16】 D. Thackray,A. C. Nelson. Semi-automatic segmentation of vascular network images using a rotating structuring element (rose) with mathematical morphology and dual feature thresholding [J]. Pattern Recogn., 1993, 12(3): 385–392.
【17】 C. Sinthanayothin,J.F. Boyce,T. H. Williamson,et al. Automated detection of diabetic retinopathy on digital fundus images. Diabetic Med. 2002,19(2): 105-112.
【18】 JJ Staal,MD Abramoff,M.Niemeijer,et al. Ridge-Based Vessel Segmentation in Color Images of the Retina [J]. IEEE Transactions on Medical Imaging, 2004, 23(4):501-509.
【19】 Monga,N. Armande,P. Montesinos. Thin nets and crest lines: Application to satellite data and medical images [J]. Computer Vision and Image Understanding,1997,67(3):285-295.
【20】 C. Sinthanayothin,J.F. Boyce,T. H. Williamson,et al. Automated detection of diabetic retinopathy on digital fundus images. Diabetic Med. 2002,19(2): 105-112.
【21】 E.Pel,R.A.Augliere,G.T.Timberlake. Feature-based registration of retinal images [J].IEEE Tran.On Med.Image,1987,6(3):272-278.
【22】 J.Park, JM Keller,PD Gader,R.Schuchard. Hough-base registration of Retinal Images[C]. Proceedings of IEEE International Conference on Systems,Man,and Cybernetics,1998:4550-4555.
【23】 Axel P,Peter D. Digital image analysis and scanning laser ophthalmoscope: registration,blobs,curves and pyramids[J]. Digital Image Processing in Medicine, Remote sensing and Visualization of Information,1992:105-117.
【24】 A.Can,C.Stewart,B.Roysam. Robust hierarchical algorithm for constructing a mosaic from images of the curved human retina[C]. Proc.IEEE Conf. Computer Vision and Pattern Recognition,1999: 286–292.
【25】 周永进,汤利容,等. 基于相位相关和非刚性光流估计的眼底 IGGA 图像精细拼合算法[J]. 仪器仪表学报, 2002,23(6):600-603.
【26】 薛 晖,周永进,万明习. 基于恒定空间分辨率离散序列小波变换的眼底血管造影图像拼接方法[J].生物物理学报,2003,19(1):63-67.
【27】 Donoso LA,Magaragal LE,Sanborn G,et al. Microcomputer application in retinal research [J]. Ophthalmology. 1982,89(1):81.
【28】 曹文轩.视网膜血管测量计[J].中华眼科杂志. 1964,11(1): 67.
【29】 http://www.pacs.com.cn/
【30】 葛广英.多媒体人眼视网膜诊断系统[J].多媒体世界,2003,35
【31】 http://www.isi.uu.nl//Research//Databases//DRIVE/.
【32】 马龙工作室. ASP+SQL Server组建动态网站实例讲解[M]. 人民邮电出版社,2004:176
【33】 电脑编程技巧与维护杂志社. Visual C++编程技巧典型案例解析-图像图像处理与数据库篇[M]. 中国电力出版社,2005,7
【34】 W. K. Pratt .Digital image processing[M] , 2nd Edition. Wiley/ Inter2
【35】 刘祝华. 图像去噪方法的研究[J]. 江西师范大学学报.2005,5:25.
【36】 黄淑英.视网膜血管宽度测量[D].西安理工大学:西安理工大学硕士学位论文,2005,3
【37】 刘斌,马天予等.数字图像处理(第二版)[M]. 人民邮电出版社,2002,12:156-169
【38】 Cheng H D, Chen Y H, Jiang X H. Thresholding using two2di2 mensional histogram and fuzzy entropy principle.[J ]. IEEE Transactions on Medical Imaging, 2000, 19(4):732-735
【39】 Brown L.G.A Survey of image resitration technique. ACM Computng Surveys,1992,24(4):325-376
【40】 高智勇,李义兵,钱勇先,林家瑞.多模态医学图像配准技术的分类与研究进展[J ].国外医学生物医学工程分册, 2000,23(4):206-211
【41】 济芳.Visual C++小波变换技术与工程实践[M].人民邮电出版社,2004,1
【42】 J.Lowell,A.Hunter,D.Steel,et al. Measurement of retinal vessel widths from fundus images based on 2-D modeling [J]. IEEE Trans. On Medical Imaging,2004,23(10):1196-1204.
【43】 L. Gang, O. Chutatape,M. Krishnan. Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter [J]. IEEE Trans on Medical Imaging. 2002,49(3): 168-172.
【2】 杨钧主编.现代眼科手册(第二版)[M].人民卫生出版社,1996
【3】 中山医学院主编.眼科学[J],人民卫生出版社,1980
【4】 http://www.oculist.net/Article/ShowNew.asp?page=40
【5】 http://hbg.hongbage.net/zhenshi/index.php?zhenshiid=17499
【6】 http://www.eye120.cn/
【7】 A.Can,H.Shen,J.N.Turner,H.L.Tanenbaum,d B.Roysam. Rapid automated tracing and feature extraction from live high-resolution retinal fundus images using direct exploratory algorithms [J]. IEEE Trans. on Info. Tech. for Biomedicine, 1999,3(2): 125–138.
【8】 Y.A.Tolias, S.M.Panas. A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering [J]. IEEE Trans. Med. Image.,1998,17(2):263-273.
【9】 S. Chaudhuri,S. Chatterjee,N. Katz,M. Nelson,M. Goldbaum. Detection of blood vessels in retinal images using two- dimensional matched filters [J]. IEEE Trans. Med. Image.,1989,8 (3): 263–269.
【10】 Chutatape,L.Zheng,S. Krishman. Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters[C]. Proceeding of IEEE Int Conf Emg and Bio Society,1998,20:3144–3149.
【11】 X.Jiang,D.Mojon. Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images [J]. IEEE Trans. Pattern Anal. Machine Intell. 2003,25(1):131–137.
【12】 A.Hoover,V.Kouznetsova,M.Goldmaum. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter respone[J]. IEEE Trans Med Imaging,2000,19(3):203-210.
【13】 T.McInerney,D.Terzopoulos. T-snakes: Topology adaptive snakes [J]. Med. Imag. Anal.,2000,4 (2): 73–91.
【14】 F.Zana,J.C.Klein. Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation [J]. IEEE Trans Image Processing, 2001, 10(7):1010–1019.
【15】 J. chanussot,P. lambert. An application of mathematical morphology to road network extraction on SAR images [J]. Mathematic Morphology and Its Application to image and signal Processing. 1998:399-406.
【16】 D. Thackray,A. C. Nelson. Semi-automatic segmentation of vascular network images using a rotating structuring element (rose) with mathematical morphology and dual feature thresholding [J]. Pattern Recogn., 1993, 12(3): 385–392.
【17】 C. Sinthanayothin,J.F. Boyce,T. H. Williamson,et al. Automated detection of diabetic retinopathy on digital fundus images. Diabetic Med. 2002,19(2): 105-112.
【18】 JJ Staal,MD Abramoff,M.Niemeijer,et al. Ridge-Based Vessel Segmentation in Color Images of the Retina [J]. IEEE Transactions on Medical Imaging, 2004, 23(4):501-509.
【19】 Monga,N. Armande,P. Montesinos. Thin nets and crest lines: Application to satellite data and medical images [J]. Computer Vision and Image Understanding,1997,67(3):285-295.
【20】 C. Sinthanayothin,J.F. Boyce,T. H. Williamson,et al. Automated detection of diabetic retinopathy on digital fundus images. Diabetic Med. 2002,19(2): 105-112.
【21】 E.Pel,R.A.Augliere,G.T.Timberlake. Feature-based registration of retinal images [J].IEEE Tran.On Med.Image,1987,6(3):272-278.
【22】 J.Park, JM Keller,PD Gader,R.Schuchard. Hough-base registration of Retinal Images[C]. Proceedings of IEEE International Conference on Systems,Man,and Cybernetics,1998:4550-4555.
【23】 Axel P,Peter D. Digital image analysis and scanning laser ophthalmoscope: registration,blobs,curves and pyramids[J]. Digital Image Processing in Medicine, Remote sensing and Visualization of Information,1992:105-117.
【24】 A.Can,C.Stewart,B.Roysam. Robust hierarchical algorithm for constructing a mosaic from images of the curved human retina[C]. Proc.IEEE Conf. Computer Vision and Pattern Recognition,1999: 286–292.
【25】 周永进,汤利容,等. 基于相位相关和非刚性光流估计的眼底 IGGA 图像精细拼合算法[J]. 仪器仪表学报, 2002,23(6):600-603.
【26】 薛 晖,周永进,万明习. 基于恒定空间分辨率离散序列小波变换的眼底血管造影图像拼接方法[J].生物物理学报,2003,19(1):63-67.
【27】 Donoso LA,Magaragal LE,Sanborn G,et al. Microcomputer application in retinal research [J]. Ophthalmology. 1982,89(1):81.
【28】 曹文轩.视网膜血管测量计[J].中华眼科杂志. 1964,11(1): 67.
【29】 http://www.pacs.com.cn/
【30】 葛广英.多媒体人眼视网膜诊断系统[J].多媒体世界,2003,35
【31】 http://www.isi.uu.nl//Research//Databases//DRIVE/.
【32】 马龙工作室. ASP+SQL Server组建动态网站实例讲解[M]. 人民邮电出版社,2004:176
【33】 电脑编程技巧与维护杂志社. Visual C++编程技巧典型案例解析-图像图像处理与数据库篇[M]. 中国电力出版社,2005,7
【34】 W. K. Pratt .Digital image processing[M] , 2nd Edition. Wiley/ Inter2
【35】 刘祝华. 图像去噪方法的研究[J]. 江西师范大学学报.2005,5:25.
【36】 黄淑英.视网膜血管宽度测量[D].西安理工大学:西安理工大学硕士学位论文,2005,3
【37】 刘斌,马天予等.数字图像处理(第二版)[M]. 人民邮电出版社,2002,12:156-169
【38】 Cheng H D, Chen Y H, Jiang X H. Thresholding using two2di2 mensional histogram and fuzzy entropy principle.[J ]. IEEE Transactions on Medical Imaging, 2000, 19(4):732-735
【39】 Brown L.G.A Survey of image resitration technique. ACM Computng Surveys,1992,24(4):325-376
【40】 高智勇,李义兵,钱勇先,林家瑞.多模态医学图像配准技术的分类与研究进展[J ].国外医学生物医学工程分册, 2000,23(4):206-211
【41】 济芳.Visual C++小波变换技术与工程实践[M].人民邮电出版社,2004,1
【42】 J.Lowell,A.Hunter,D.Steel,et al. Measurement of retinal vessel widths from fundus images based on 2-D modeling [J]. IEEE Trans. On Medical Imaging,2004,23(10):1196-1204.
【43】 L. Gang, O. Chutatape,M. Krishnan. Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter [J]. IEEE Trans on Medical Imaging. 2002,49(3): 168-172.