摘要
近年来,在临床医院配置的现代大型医疗设备越来越多,医学影像技术在临床诊断与医学研究中扮演了愈来愈重要的地位。医学影像呈现出实时化、海量化、功能化的趋势。为了辅助医生临床诊断及临床研究,海量的医学影像信息必须借助于计算机分析的手段加工处理,有效地整合、加工与挖掘,才能发挥更大的作用。这使医学影像数字化分析技术变成了临床辅助诊断的重要技术,成为实现精确诊断的必需。所以,医学图像数字化分析技术与日益提高的临床诊断要求的结合非常重要。目前,对两者如何更好结合进行研究的重要性主要表现为以下两个方面。
首先,现代医疗设备很短时间内产生的大量高分辨率图像分析受到临床有限的人力资源约束,使医学图像后处理技术成为临床应用的必需。例如,与传统单排CT相比,多排CT一次成像的层厚更薄、层数更多、细节分辨率更好而且数据量大。但是,医院临床放射科的医生人数受到行政编制及培养时间的限制,所以只有依靠高级计算机后处理技术的辅助,才能更好更快地从海量的信息中提取更多有用的诊断信息,避免耽误或遗漏。
其次,新的成像技术促使新的数字化分析技术从检查科室向临床医生普及。例如,现在的动态三维(4D)能让医生和母亲看到未出生胎儿的脸部特征和在子宫内的运动。这是因为三维超声能探测、采样、数字化存储三维的回声信号,并能渲染绘制出活灵活现的未出生胎儿的图像。四维超声还能实时地接收和显示这种三维图像,帮助医生研究胎儿的移动与活动规律。对临床医生来说,利用四维超声能观察胎儿健康状况和微小的移动,可以在屏幕上对未出生的胎儿做从头到脚的健康评估,就象儿科医生直接面对已出生的婴儿一样。通过这样对未出生婴儿的体位变化以及呼吸的观察,医生能更容易诊断。但是,受到医院信息化的限制及医学影像处理软件普及的限制,上述功能还只能在检查科室的工作站上完成,而不能在临床医生工作站上实现,这极大限制了医学影像新技术的普及和新技术对临床辅助诊断的效果。
为此,结合本人在医院设备科,医工结合一线工作十余年的经验,本文主要是针对临床环境的医学影响新技术的应用作了有针对性的研究,其主要内容如下:
第一.提出利用基于广义模糊算子(GFO)的边缘检测算法来改进标记点分水岭分割。传统的分水岭分割一般是在原始图像中根据边缘检测算子所得的边缘图进行计算,常规的边缘检测算子并没有引入图像的先验信息或形状约束。由于病理改变以及医学影像数据内在的模糊性,常规边缘检测算子很难引导分水岭算法收敛到正确的目标轮廓,由此导致传统分水岭算法容易受到图像噪声的干扰而“过分割”。在此情况下,提出利用基于广义模糊算子(GFO)的边缘检测算法来改进标记点分水岭分割。从实验结果看,本文所提出的方法非常适合应用于临床医学图像分割。
第二.提出了利用Gibbs距离图(DM)Snake模型分割医学图像的算法。利用Gibbs距离图(DM)Snake模型分割医学图像能克服医学图像固有的噪声和伪边缘干扰,收敛到正确的目标轮廓。该方法首先推导Gibbs形态学梯度,然后提出基于Gibbs形态学梯度的距离图Snake模型的医学图像分割方法。实验结果表明,本文所提出的算法克服了传统距离图Snake模型的上述缺点。本文所提出的方法分割结果鲁棒性好,分割过程无须人工干预,适合应用于临床医学图像分割。
第三.提出了对医学图像进行分区显示和增强的新方法。对医学图像进行分区显示和增强具有显著的意义。为了改善临床医学图像的显示质量,增强灰度显示范围,把图像划分为不同的区域灰度增强显示具有实际的意义。考虑到人体同一类组织往往是相邻的,不同的组织之间往往可以通过分割方法分隔开,采用基于分区的灰度映射方法可以针对各种组织的灰度特性建立灰度映射关系,这样虽然人肉眼能看到的灰度分辨率仍然只有256级,但是从图像上实际能看到的远远超过了256级。基于分区的图像增强同样具有很好的改善图像显示质量的效果,文中也对这个内容进行了讨论。
第四.针对在内容三中提出的方法,为了提高显示速度,达到实时,满足临床的实际需要。我们还提出了基于GPU加速的算法:算法改进的加速性在于由片元作色器并行执行的逐点计算,取代了CPU串行执行的逐点计算,大大减小了CPU的负载,效率高于单独采用CPU的处理效率,运算接近实时,大大提高了算法在临床的实用性。
伴随着成像技术的发展,医学影像数字化分析技术与临床的实际需求结合已从热门的研究领域,逐步发展成实现精确诊断必需的过程。为此,本论文研究的意义如下:
提高临床人力资源工作效率的需要:成像设备采集的海量影像信息,如不整合、加工与处理,信息仍然是孤立的、低质的,如此繁杂无序的信息往往使得临床医生感觉盲然无措,这不利于临床诊断,不利于病因的有效与定量分析,不利于对疾病的新发展与新认识。目前,医学影像分析技术与临床的实际结合正成为生物医学工程研究的热点。但是,目前的方法与临床的实际使用的需求还有一定的差距,自动化与时间上还需提升。若想进一步提高诊断质量,有赖于诸如图像配准、信息融合、病灶分割、运动估计及其可视化等技术在临床的真正普及应用。
符合医院数字化建设的需要:数字化医院是将最先进的IT技术充分应用于医疗,其核心是通过宽带网络把数字化医疗设备、数字化医学影像系统和数字化医疗信息系统等全部临床作业过程纳入到数字化网络中,实现临床作业的无纸化和无片化运行。医学图像通信与归档系统(PACS)是数字化医院的主要组成之一,PACS系统完成了医学影像的存储、通信功能,从而实现了数字影像的共享,打破了数字影像只由影像科独占的局面,但当前医学影像的数字化分析技术并没有实现共享,临床医生在PACS看到的只是由设备采集的原始二维图像数据,若欲进行进一步的分析,需要到影像科室由设备厂家提供的为数不多工作站上进行,而这些工作站的使用权又往往集中在少数医生手中,这严重影响了医学影像数字化分析技术在临床诊断中作用的发挥。相关的研究正是为了有效解决这一需求,从而推动医院的数字化建设。
本文在这个领域做了一些有益的探索,希望能为医学图像处理技术在临床普及应用的广阔发展前景做出自同益微薄的贡献。
In recent years,more and more modem medical imaging equipments have been used in hospitals.Medical imaging technologies are playing more and more important roles in clinical diagnosis and medical research.Multi-dimension real-time screening of medical imaging is becoming necessary in hospitals.However,for better aiding clinical diagnosis,high volume medical information must be processed,integrated and mined effectively,which makes intelligent analysis of multi-dimension medical data necessary in daily clinical work.Thus how to combine clinical diagnosis with current research of medical radiology is very important,which can be shown in two reasons mentioned below.
Firstly,high resolution images,ultra-fast scanning speed and a broad range of clinical applications are limited by human resource in hospitals.For example, multi-slice CT captures significantly much larger data sets and yields much sharper, more detailed images than traditional CT.But restricted by some well-known reasons, radiologists need to review large image sets quickly and easily with the help of sophisticated medical imaging process technology that can get valuable information from advanced multi-slice images,to avoid missing important diagnostic information in limited times.
Secondly,new medical imaging technology should become popular not only in diagnostic department,but in clinical department.For example,now four-dimensional (4D) ultrasound lets women and doctors look at facial features and watch the growing baby move.With 3D ultrasound,a volume of echoes is taken,stored digitally,and shaded to produce life-like images of the fetus.A 4D ultrasound takes the images produced by 3D ultrasound and adds the element of movement.Now,the life-like pictures can move and the activity of the fetus can be studied.To doctors,4D reveals more detail about fetal health and small movement.Just as a pediatrician begins an exam by observing a newborn,doctors assess the fetus from head to toe on screen.By watching him or her shift position and breathe,doctors can check for problems.But restricted by practical situations in most hospitals,the above mentioned functions can not be utilized by clinical doctors.To a large extent,this has limited popularity and effect of new medical imaging analysis technologies in clinical use much.
For this purpose,the thesis focuses mainly on research of practical application of medical imaging analysis in clinical environment.Main topics are listed below.
First,a new medical image segmentation algorithm - marker-controlled watershed segmentation using the edge-detecting algorithm based on Generalized Fuzzy Operator is proposed in the article.Usually,traditional watershed segmentation is implemented based on the edge-extracted image from the original image.However, the conventional edge-detecting algorithms have not the prior information as well as shape restriction.Because of the changing pathology and the inherent property of fuzzy imaging data,the conventional edge-detecting can hardly guide watershed algorithm converging into the correct object contour;the resultant watershed algorithm is subjected to image noise and over-segmentation.In this paper,we propose to improve marker-controlled watershed segmentation using the edge-detecting algorithm based on Generalized Fuzzy Operator.Judginging from the results,the proposed method is very adaptive to the segmentation of the clinical images.
Second,segmentation of medical images based on Gibbs morphological gradient and distance map(DM) Snake model is proposed to find right contours of objects when processing medical images with noises and pseudo-edges.To begin with,Gibbs morphological gradient is introduced.Then segmentation based on Gibbs morphological gradient and distance map snake model is proposed.Experimental results show that the new segmentation algorithm of medical images,based on Gibbs morphological gradient and distance map snake model can suppress noises and pseudo-edges.A conclusion can be drawn that advantages of the method are robust to image noise,easy to be implemented in clinical image segmentation with only a few user interventions.
Third,a new method-region-based image display and enhancement for improving image display quality is introduced.It is useful to improve image display quality and enhance image based on different regions in the image,as similar human organs are often clustered.Different human organs are separated by image segment method.Then each area indicating different human organs are mapped to 1-256 gray level with different transformation functions.Thus,more than 256 levels of gray information can be show for clinical diagnosis.Region-based image enhancement is also very useful and is discussed in the paper.
Fourth,acceleration research of the method mentioned above is done based on Graphics Processing Unit(GPU).Now GPU is used as general-purpose processing unit in many other fields,because it can become a general-purpose computing engine without compromising its performance.The combination of a data parallel engine with more of the general-purpose flexibility of a traditional CPU offers a powerful model for our method,which consists of a mix of irregular matrix math and other logic.Thus,region-based image enhancement for improving image display quality can be run in real-time in clinical environment.
All in all,with the rapid development of modem medical imaging equipments, how to use intelligent analysis of multi-dimension medical data in daily clinical work is becoming more and more crucial.Some useful methods are studied especially based on clinic questions proposed and tested in the paper.
引文
[1]Colchester A,Zhao J,et al.Development and Preliminary Evaluation ofVislan.A Surgical Planning And Guidance System Using Intra-Operative Video Imaging[J].Medical Image Analysis,1996,191(5):73-90.
[2]Grimson W,Ettinger G,et al.Utilizing Segmented MRI Data in Image Guided Surgery[C].IJPRAI,1997,11(8):1367-1397.
[3]Toole R.Assessing Skill and Learning in Surgeons And Medical Students Using A Force Feedback Surgical Simulator[C].Medical Image Computing and Computer Assisted Intervention(MICCAI),1998,441-447.
[4]Warfield S.and Kikinis R.Adaptive Template Moderated Spatially Varying Statistical Classification[C].Medical Image Computing and Computer Assisted Intervention(MICCAI),1998,231-238.
[5]Muller HW.Links JM,et al.Measurement of Radiotracer concentration in Brain Gray Matter Using Positron Emission Tomography:MRI-Based Correction for Partial Volume Effects[C].Cereb.Blood Flow Metab,1992,571-583.
[6]Joseph YH,Marios G,Mia KM,et al.Computer-aided Classification of Breast Microcalcification Clusters:Merging of Features from Image Processing and Radiologists[J].Med.Phys,2002,29(5):475-483.
[7]Haralick RM and Shapiro LG.Image Segmentation Techniques[J].Comput.Vis.Graph.Im.Proc.,1985,29(7):100-132.
[8]Pal LG.and Pal SK.A Review on Image Segmentation Techniques[J].Patt.Rec,1993,26:1277-1294.
[9]叶更新,曾旭文,范绍鸣.螺旋CT三维和多层面重建在髋关节创伤中应用16例分析[J].暨南大学学报,2003,24(2):97-1001.
[10]谭永忠,黄余忠,罗新忠,袁春霞.多层螺旋CT三维重建的应用价值[J].中国冶金工业医学杂志,2007,24(5):585-586.
[11]Van der Geest,et al.Evaluation of a new method for automated detection of left ventricular boundaries in time series of magnetic resonance images using an Active Appearance Motion Model[J].Cardiovasc Magn Reson,2004,6(3):609-617.
[12]Sugioka K,et al.Rapid and accurate noninvasive assessment of global left ventricular systolic function using biplane advanced automated contour tracking method[J].Am Soc Echocardiog,2003,16(12):1237-1243.
[13]Van der Geest,Reiber JH.Quantification in cardiac MRI[J].Magn Reson Imaging,1999,10(5):602-608.
[14]Wise RG,et al.Geometrical models of left ventricular contraction from MRI of the normal and spontaneously hypertensive rat heart[J].Phys Med Biol,1999,44(10):2657-2676.
[1]赵喜平.磁共振成像[M].北京:科学出版社,2004.
[2]张雪林.磁共振成像(MRI)诊断学[M].北京:人民军医出版社,2004.
[3]俎栋林.磁共振成像学[M].北京:高等教育出版社,2004.
[4]章毓晋.图象分割[M].北京:科学出版社,2001.
[5]罗述谦,周果宏.医学图象处理与分析[M].北京:科学出版社,2003.
[6]Pham DL,Xu C,Prince JL.A survey of current methods in medical image segmentation[J].Annual Review of Biomedical Engineering,2000,2,315-337.
[7]Liew,Alan WC,Yan H.Current methods in the automatic tissue segmentation of 3D magnetic resonance brain images[J].Current Medical Imaging Reviews,2006,2(1):91-103.
[8]颜刚.基于模糊马尔可夫场的图像算法研究[D].广州:南方医科大学,2005.
[9]赵荣椿,迟耀斌,朱重光.图像分割技术进展[J].中国体视学和图像分析,1998,3(2):121-128.
[10]赵志峰,张尤赛.医学图像分割综述[J].华东船舶工业学院学报,2003,17(3):43-48.
[11]聂斌.医学图像分割技术及其进展[J].泰山医学院学报,2002,23(4):422-426.
[12]Bezdek J,Hall L,Clarke L.Review of MR image segmentation techniques using pattern recognition[J].Medical Physics,1993;20:1033-1048.
[13]Clarke LP,Velthuizen RP.MRI segmentation:methods and applications[J].Magnetic Resonance Imaging.1995,13(3):343-368.
[14]李璟,朱善安,He B.医学图像分割技术[J].生物医学工程学杂志,2006,23(4):891-894.
[15]Goshtasby A,Turner DA.Segmentation of cardiac cine MR images for extraction of right and left ventricular chamber[J].IEEE Trans.On Medical Imaging,1995,14(1):56-64.
[16]Thedens DR,Skorton DJ,Fleagle SR.Methods of graph searching for border detection in image sequences with applications to cardiac magnetic resonance imaging[J].IEEE Trans.On Medical Imaging,1995,14(1):42-55.
[17]Canny J.A computational approach to edge detection[J].IEEE Trans.On Pattern Analysis and Machine Intelligence,1986,8(6):679-698.
[18]Deng W,Iyengar SS.A new probabilistic relaxation scheme and its application to edge detection[J].IEEE Trans.On Pattern Analysis and machine Intelligence,1996,18(4):432-437.
[19]Qian R J,Huang TS.Optimal edge detection in two-dimensional images[J].IEEE Trans.On Image Processing,1996,5(7):1215-1220.
[20]付宜利,高文朋,王树国.颅脑磁共振图像分割评价的统计学方法[J].医学影像学杂志,2005,15(8):644-647.
[21]Bansal R,Staib LH,Chen Z,et al.Entropy-based,multipleportal-to-3D CT registration for prostate radiotherapy using iteratively estimated segmentation[C].In:Proceedings of Medical Image Computing and Computer-Assisted Intervention, Cambridge, UK, 1999: 567-578.
[22] Sahoo P, Wilkins C, Ueager J. Threshold selection using Rrenyi's entropy [J]. Pattern Recognition, 1997, 30(1): 71-84.
[23] M. Singh, P. Patel, D. Khosla, and T. Kim. Segmentation of functional MRI by K-means clustering [J]. IEEE Trans. Nucl. Sci., 1996, 43(6): 2030-2036.
[24] Mohamed NA, Sameh MY, Nevin M. A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data [J]. IEEE Trans. On Medical Imaging, 21(3), 2002:193-199.
[25] Liew AW, Yan H. An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation [J]. IEEE Trans. On Medical Imaging, 2003, 22(9): 1063-1075.
[26] Zhao YQ, Li ML. A modified fuzzy C-Means algorithm for segmentation of MRI[C]. Proceedings of the 5th ICCIMA'03, 2003.
[27] Nie SD, Chen Y, Gu SD, et al. The study of fast fuzzy clustering segmentation algorithm of head MRI [J]. Chinese Journal of Biomedical Engineering, 2001, 20(2): 104-109.
[28] Wan S Y, William H. Symmetric region growing [J]. IEEE Trans, on Image Processing, 2003, 12(9): 1007-1015.
[29] Rosenfeld A. Fuzzy digital topology [J]. Inform & Control, 1979, 40: 76-87.
[30] Rosenfeld A. On connectivity properties of grayscale pictures [J]. Pattern Recognition, 1983,16(1):47-50.
[31] J. K. Udupa, S. Samarasekera. Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation [J]. Graphical model and Image processing, 1996, 58(3): 246-261.
[32] J. K. Udupa. Fuzzy connectedness and image segmentation [J]. Proceedings of the IEEE, 2003,91(10): 1649-1669.
[33] Yang FG, Tianzi Jiang TZ, Pixon-based image segmentation with Markov random fields[J]. IEEE Trans, on Image Processing, 2003, 12(12): 1552-1559.
[34] Geman S, Geman D. Stochatic relaxation, Gibbs distributions, and the bayesian restoration of images [J]. IEEE Trans. On Pattern Analysis and Machine Intelligence,1984,6(6),721-741.
[35]Zhang Y,Brady M,Smith S.Segmentation of brain images through a hidden Markov random field model and the expectation-maximization algorithm[J].IEEE Trans.On Medical Imaging,2001,20(1):45-57.
[36]Li SZ.Markov random field modeling in image analysis[M].Sptinger-Verlag,2001.
[37]Lakshmanan S,Derin H.Simultaneous parameter estimation and segmentation of Gibbs random fields using simulated annealing[J].IEEE Trans.On Pattern Analysis and machine Intelligence,1989,121(8):799-813.
[38]Melas DE,Wilson SP.Double Markov random fields and bayesian image segmentation[J].IEEE Trans.On Signal Process,2002,50(2):357-365.
[39]Kass M,Witkin A,Terzopoulos D.Snakes:active contour models[C].In:Proc of First Int Confon Computer Vision.London,1987.259-269.
[40]Osher S,Sethian JA.Fronts propagating with curvature-dependent speed:algorithms based on Hamilton-Jacobi formulations[J].Journal of Computational Physics,79(1),1988:12:49.
[41]Wang Z,Vemuri BC.DTI segmentation using an information theoretic tensor dissimilarity measure[J].IEEE Trans.On Medical Imaging,2005,24(10):1267-1277.
[42]陈强,周则明,王平安等.带标记线左心室MR图像的自动分割[J],中国图形图像学报,2004,9(6):666-673.
[43]Zhukov L,Museth K,Breen D,et al.Level set modeling and segmentation of.DT-MRI brain data[J],Journal of Electronic Imaging,2003,12(1):125-133.
[44]Wang Z,Vemuri BC.DTI segmentation using an information theoretic tensor dissimilarity measure[J].IEEE Trans.On Medical Imaging,2005,24(10):1267-1277.
[45]孙优贤,卢建刚,许锋.神经网络在图像处理中的应用[J],信息与控制,2003,32(4):344-350.
[46]Ahmed MN,Farag AA.Two-stage neural network for volume segmentation of medical images[J].Proceedings of IEEE International Conference On Neural Networks,1997(3):1373-1377.
[47] Chen CT. Medical image segmentation by a constraint satisfaction neural network[J]. IEEE Trans. On Nuclear Science, 1991, 38(2): 678-686.
[48] Cheng KS. The application of competitive Hopfield neural network to medical image segmentation [J]. IEEE Trans. On Medical Imaging, 1996(15): 560-567.
[49] Mallat S. A theory for multiresolution signal decomposition: the wavelet representation [J]. IEEE Trans. On Pattern Analysis and machine Intelligence, 1989, 11(7): 647-693.
[50] Laine A, Fna J. Texture classification by wavelet packet signatures [J]. IEEE Trans. On Pattern Analysis and machine Intelligence, 1993, 15(11): 1186-1191.
[51] Chun DN, Yang HS. Robust image segmentation using genetic algorithm with a fuzzy measure [J]. Pattern Recognition, 1996, 29(7): 1195-1211.
[52] Bram VG, Alejandro FF, Joes JS, et al. Active shape model segmentation with optimal features [J]. IEEE Trans. On Medical Imaging, 21(8): 924-933.
[53] Vincent L, Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations [J]. IEEE Trans. On [53] Pattern Analysis and machine Intelligence, 1991, 13(6): 583-598.
[54] Udupa JK, Leblanc VR, Zhu Y, et al. A framwork for evaluating image segmentation algorithms [J]. Computerized Medical Imaging and Graphics, 2006, 30(1):75-87.
[55] Zhang YJ. A survey on evaluation methods for image segmentation [J]. Pattern Recognition, 1996, 29(8): 1335-1344.
[1]Pal NR,Pal SK.A review on image segmentation techniques[J].Patt Recogni,1994,26(9):1277-94.
[2]Kass M,Witkin A,Terzopoulous D.Snakes:active contour models[J].Int J Comp Vis,1988,1(4):321-331.
[3]Hou ZQ,Han CZ.Force field analysis snake:an improved parametric active contour model[J].Part Recogni Letters,2005,26(5):513-26.
[4]Cohen LD,Cohen I.Finite-element methods for active contour models and balloons for 2-D and 3-D images[J].IEEE Trans on PAMI,1993,15(11):1131-47.
[5]Ma WY,Manjunath BS.EdgeFlow:a technique for boundary detection and image segmentation[J].IEEE Trans on Image Proc,2000,9(8):1375-1388.
[6]J.Serra.Image analysis and mathematical morphology[M].London:Academic Press,1982
[7]Ji Z,Ming Z,Reducing Periodic Noise Using Soft Morphology Filter[J].J Elect (China),2004,21(2):159-62.
[8]D.Geiger,A.Yuille.A common framework for image segmentation[J].Int J Comp Vis,1991,6(3):227-243.
[9]D Huttenlocher,G Klanderman,W Ruchlidge.Comparing images using the hausdorff distance[J].IEEE Trans on PAMI,1993,15(9):850-63.
[10]龚剑,陈武凡.基于广义模糊吉波斯随机场的噪声图象分割[J].南方医科大学学报2006,26(4):390-393.
[11] Gong Jian, Chen WF. Noise image segmentation based on generalized fuzzy Gibbs random field [J]. J South Med Univ, 2006, 26(4): 390-393.
[1]Pal NR,Pal SK.A review on image segmentation techniques[J].Pattern Recognition,1994,26(9):1277-1294.
[2]HT Nguyen,M Worring,R van den Boomgaard,Watersnakes:Energy-driven watershed segmentation[J].IEEE transactions on pattern analysis and machine intelligence.March 2003,25(3):330-342
[3]Luc Vincent,Pierre Soille.Watersheds in digital spaces:an efficient algorithm based on immersion simulations[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1991,13(6):583-598.
[4]Lin YC,Tsai YP,Hung YP,Comparison between immersion-based and toboggan-based watershed image segmentation[J].IEEE Transactions on Image Processing,March 2006,15(3):632-640.
[5]CHEN Wu-Fan,LU Xian-Qing,CHEN Jian-Jun,and Wu Guo- Xiong,A New Algorithm of Edge Detection for Color Image:Generalized Fuzzy Operator,SCIENCE in China[J],1995,38(10):1272-1280.
[6]袁华,吴效明,等.GFO在边缘检测中的关键参数定量计算研究,电子学报[J].2001,29(7):888-890.
[7]Yuan Hua,Wu Xiaoming,etc.The Quantitative Calculation of Key Parameters of GFO Used in Edge Detection, ACTA ECTRONICA SINICA[J], 2001,29(7): 888-890
[8] Luc Vincent, Morphological Grayscale Reconstruction in Image Analysis: Applications and Efficient Algorithms, IEEE Transactions on Image Processing[J], 1993,2(2): 176-201.
[9] V Grau, AUJ Mewes, M Alcaniz, R Kikinis, Improved Watershed Transform for Medical Image Segmentation Using Prior Information[J], IEEE Transactions on Medical Imaging, 2004, 23(4): 447-458.
[1]章毓晋.图像工程(上册)-图像处理和分析[M].北京:清华大学出版社,1999.76-81.
[2]Sherrier R H,Johnson G A.Regionally adaptive histograme equalizat ion of the chest[J].IEEE T rans MedImag,1987,126(6):1-7.
[3]GONZALEZ R C,WOODS R E.Digital image processing[M].Second Edition,Publishing House of Electronics Industry,2002:77-137.
[4]SILVA CENTENO J A,HAERTEL V.Anadaptive image enhancement algorithm[J].Pattern Recognition,1997,30(7):1183-1189.
[5]DEMIGNYD.On optimal linear filtering for edge detection[J].Image Processing,2002,11(7):728-737.
[6]LEE C S,KOU Y H,YU P T.Weighted fuzzymean filter for image processing[J].Journal of Fuzzy Sets and Systems,1997,89(1):157-180.
[7]National NEMA standards publication PS 3.14.Digital imaging and communications in medicine(DICOM),Part 14:Grayscale Standard Display Function[C]Washington.D.C:National Electrical Manufacturers Association,1998:37-38.
[8]Working group 11 display:Digital imaging and communications in medicine (DICOM).DICOM standards committee[S]Supplement 33:Grayscale Sof tcopy Presentation State Storage,Final text,1999.
[9]Jone Goutsias,Markov Random Fields:Interacting Particle Systems for Statistical Image Modeling and Analysis[J],Submitted to Proceedings of IEEE,Feb.1996.
[10]S.Geman and D.Geman,Stochastic Relaxation,Gibbs Distribution,and Bayesian Restoration of Images[J],IEEE TPAMI,1984,6(11):721-741.
[11]H Derin and H Elliott,Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields[J],IEEE PAMI,1987,9(1):39-55.
[12]Stan Z,Li,Markov Random Field Modeling in Image Analysis[M],Spmger Verlag.Tokyo 2001
[13]Elliott,H.,H.Derin,R.Cristi,Application of the Gibbs Distribution to Image Segmentation[C],In proceedings of the International Conference on Acoustic,Speech and Signal Processing,San Diego,32.5.1-32.5.4.
[14]Derin,H,W.S.Cole,Segmentation of Textured Image Using Gibbs Random Fields[M],Computer Vision,Graphics and Image Processing 35,72-98.
[15]J.C.Dunn,A Fuzzy Relative of the ISODATA Process and Its Use in detecting Compact Well-Sparated Clusters[J],Journal of Cybernetics,1973,3(2):32-57.
[16]Bezdek JC,A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms[J],IEEE TPAMI,1980,2(1):1-8.
[17]Mallat,S.G.,A theory for multiresolution signal ecomposition:the wavelet representation[J],IEEE PAMI,1988,11(7):674-693.
[18]陈武凡.小波分析及其在图像处理中的应用[M].北京:科学出版社,2002,167-73.
[19]David Luebke,SIGGRAPH 2004 GPGPU COURSE,http://www.gpgpu.org/s2004/[OL],2004
[20]John D.Owens,David Luebke,et al A Survey of General-Purpose Computation on Graphics Hardware[A].Eurographics 2005[C],State of the Art Reports,August 2005,21-51.
[21]Govindaraju NK,Sud A,Yoon SE,Manocha D.SWITCH:Parallel occlusion culling for interactive walkthroughs using multiple GPUs. Technical Report, TR02-027, UNC-CH, 2002.
[22] Govindaraju NK, Redon S, Lin M, Manocha D. CULLIDE: Interactive collision detection between complex models in large environments using graphics hardware[C]. In: Proc. of the Eurographics/SIGGRAPH Workshop on Graphics Hardware. 2003. 25-32.
[23] Sud A, Otaduy MA, Manocha D. DiFi: Fast 3D distance field computation using graphics hardware[C]. In Proc. of the Eurographics, 2004.
[24] Tomov S, McGuigan M, Bennett R, Smith G, Spiletic J. Benchmarking and implementation of probability-based simulations on Programmable graphics cards[J]. Computers & Graphics,2005,29(1):380-387.
[1]David Luebke,SIGGRAPH 2004 GPGPU COURSE,http://www.gpgpu.org/s2004/[OL],2004
[2]John D.Owens,David Luebke,etc,A Survey of General-Purpose Computation on Graphics Hardware[A].Eurographics 2005[C],State of the Art Reports,August 2005,21-51.
[3]Govindaraju NK,Sud A,Yoon SE,Manocha D.SWITCH:Parallel occlusion culling for interactive walkthroughs using multiple GPUs.Technical Report,TR02-027,UNC-CH,2002.
[4]Govindaraju NK,Redon S,Lin M,Manocha D.CULLIDE:Interactive collision detection between complex models in large environments using graphics hardware.In:Proc.of the Eurographics/SIGGRAPH Workshop on Graphics Hardware.2003,25-32.
[5]Sud A,Otaduy MA,Manocha D.DiFi:Fast 3D distance field computation using graphics hardware[M].In:Proc.of the Eurographics,2004.
[6]Tomov S,McGuigan M,Bennett R,Smith G,Spiletic J.Benchmarking and implementation of probability-based simulations on Programmable graphics cards[J].Computers & Graphics,2005,29(1)
[7]Steve Upstill,The RenderMan Companion[M],Addison-Wesley Publishing Company,1990.
[8]Rost RJ.OpenGL Shading Language[M].Addison-Wesley,2001.Proudfoot K,Mark WR,Hanrahan P,Tzvetkov S.A real time procedural system for Programmable graphics hardware.Computer Graphics,159-170.
[9]Peeper C,Mitchell JL.Introduction to the DirectX 9 High-Level Shader Language.2003.http://msdn.microsoft.com/library/default.asp?url=/library/en-us/dnhlsl/html/Shaderx2_introductionto.asp[OL].
[10]Mark WR,Glanville S,Akeley K,Kilgard MJ.Cg:A system for Programming graphics hardware in a C-like language[J].ACM Trans.on Graphics,2003,22(3):896-907.
[11]Buck I,Foley T,Horn D,Sugerman J,Fatahalian K,Houston M,Hanrahan P.Brook for GPUs:Stream computing on graphics hardware[J].ACM Trans.on Graphics,2004,23(3):777-786.
[12]McCool M,Toit SD,Popa T,Chan B,Moule K.Shader algebra.ACM Trans.on Graphics[J],2004,23(3):787-95.
[13]唐圣泽.三维数据场可视化技术[M].北京:清华大学出版社,1996.
[14]http://www.nvidia.com[OL].
[1]田捷,包尚联,周明全.医学影像处理与分析[M].北京:电子工业出版社,2003.
[2]William R.Hendee.Physics and applications of medical imaging[J].Reviews of Modern Physics,71(2):444-450.
[3]Dewey M,Rutsch W,et al,Coronary artery stenosis quantification using multislice computed tomography.Invest Radio.2007 Feb;42(2):78-84.
[4]Chang CH,Yu CH,et al.Predicting fetal growth restriction by humerus volume:A three-dimensional ultrasound study[J].Ultrasound Med Biol.,2006,32(6):791-795.
[5]Matsumoto K,et al.Cerebral gangliogliomas:clinical characteristics[J],CT and MRI.ACTA NEUROCHIRURGICA,1999,141(12):135-141.
[6]Kay CL,Hawes RH,Virtual endoscopy-comparison with colonscopy in the detection of space-occupying lesions of the colon[J],2000,32(03):226-232.
[7]L Volterrani,et al Three-dimensional analysis of pulmonary nodules by MSCT with Advanced Lung Analysis(ALA1) software[J].Radiol Med.2006,111(3):343-354.
[8]廖琪梅,杭洽时,钱宗才,汪敏,王舒宜.三维医学图象重建的方法与应用现代电子技术[J].1997,(42)3:41-42.
[9]V annier M W,M arsh J L,W arren J O.Three Dimensional Computer Graphics for Craniofacial Surgical Planning and Evaluation.ACM SIGGRAPH Computer Graphics,1983,17(3):263-273.
[10]J.D.Owens,D.Luebke,et al,A survey of general-purpose computation on graphics hardware[R],in Proceedings of Eurographics 2005-State of the Art Reports,2005.
[11]E.Lefohn,J.M.Kniss,et al,Interactive deformation and visualization of level set surfaces using graphics hardware[J],in Proceedings of IEEE Visualization,2003,29(10):75-82.
[12]林亚忠,陈武凡,杨丰等.基于混合金字塔吉伯斯随机场的图像分割[J],中国生物医学工程学报,2004,23(1):79-82.
[13]林亚忠,陈武凡,杨丰等.基于广义模糊吉伯斯随机场图像分割新算法[J],计算机学报,2003,26(11):1464-1469.