医学图像增强与插值的算法研究
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摘要
医学图像处理作为一门具有很强实用价值和广泛应用前景的交叉学科,吸引了电子、数学、物理等诸多不同专业人员从事于这一热点领域的研究。医学图像具有信息量大、图像模糊、处理困难等特点,借助于图形图像技术的快速发展和应用,医学图像的成像质量和显示方法得到了极大的改善,大大提高临床诊断的准确性和正确性,这样既能充分发挥了医学影像设备的效力和潜能,又能促进了诊疗水平的提高,改善了人们群众的健康状况。因此对医学图像处理方法的研究具有十分重要的理论意义和现实意义。
本文以医学图像处理过程中的实际需求为目标,侧重于医学图像处理的算法研究。通过利用多尺度几何分析方法、线性和非线性结构张量等新的数学工具,针对乳腺X线图像增强、扩散张量图像插值放大和图像去噪等问题进行了研究,结合医疗设备获取的图像的特点建立了合理的数学模型,并提出相应的求解算法和快速实现格式以改善图像质量,为有效诊断病情提供科学合理的依据和后续进一步研究奠定良好基础。本文的主要研究内容和创新点包括以下几个方面：
1.介绍了多尺度几何分析方法的基本理论,分析了后小波时代几种典型多尺度几何分析方法的优缺点。在此基础上着重研究了Contourlet变换及其性质,利用多方向多分辨分析理论证明了由双正交滤波器组生成的Contourlet分解中Contourlet基函数族构成了L2(R2)的Riesz基,进一步完善了Contourlet变换理论。
2.提出了基于Contourlet变换的乳腺X线图像增强算法。乳腺X线照相技术是针对无明显症状的乳腺癌患者进行早期检测的有效方法之一。不过,通常乳腺X线照片只能显示检测到的信息的3%,主要的原因在于正常腺状组织和恶性肿瘤之间的对比度过低。因此为了增强乳腺X线照片的对比度以提高图像的质量,首先通过利用Contourlet变换的多分辨多方向特性对乳腺X线照片进行Contourlet分解,再选取合适的阈值函数和非线性增益函数对Contourlet系数作相应的修改,完成图像去噪和增强,突出显示了表现为早期乳腺癌的微小钙化块等感兴趣的区域。最后,还利用基于标准偏差的评价指标比较了不同增强算法的处理效果。仿真实验表明,本文的算法增强的乳腺X线图像效果明显,不仅为医生诊断提供了有力的帮助和参考,而且为后续图像分割、特征提取等其它应用奠定了良好基础。
3.对扩散张量图像的插值放大进行了研究。为得到更加精细的张量场估计,结合张量图像自身的结构特性,给出了基于梯度特征和线性结构张量的两种各有优点的插值方法,前者速度快,后者精度更高,并研究了插值算法的快速实现格式。最后,文中还指出基于结构张量的图像插值是包含了一种基于偏微分方程插值算法的更广义的插值框架。仿真实验表明,与传统的线性插值相比,这两种插值算法得到的结果更加精细,有效地保留了原始信号的边缘等结构特征,有利于跟踪大脑纤维束等进一步的研究。
4.研究对图像有效去噪的算法。在线性结构张量的基础上,进一步讨论了非线性结构张量,并结合Wiener滤波给出了一种新的图像去噪算法及其快速实现,减少了噪声的影响。
最后,对整个论文的工作和研究成果进行了总结,讨论了一些有待于进一步研究的问题以及下一步的研究设想和目标。
As an interdisciplinary field with practical value and wide application prospect, medical images processing has attracted various specialized staff in electronics, mathematics and physics to participate in its research. Medical images are characterized by large information amount, fuzziness and processing complexity. Though, owing to the fast development and application of computer graphics and image processing, the imaging quality and displaying method have been greatly improved along with the accuracy and correctness of clinical diagnosis, which not only brings the effectiveness and potential of medical imaging equipment into full play, but also improves the diagnostic and therapeutic level as well as people's health. Hence, it's of great realistic and theoretical significance to research on medical images processing.
Aiming at the practical requirement in medical images processing, the dissertation focuses more on the study of algorithms. By such mathematical tools as multiscale geometric analysis and structure tensor, research is done on mammographic image enhancement, diffusion tensor image interpolation and image filtering, proper mathematical models are built based on the characteristics of the images acquired by the medical equipment, corresponding algorithm and fast implementation are presented to improve image quality, thus offering scientific references for effective diagnosis and a good foundation for further study. The main work and contributions of the dissertation are outlined as follows:
1. Introduces the basic theory of multiscale geometric analysis, analyses the advantages and disadvantages of several typical transforms, such as bandelet, curvelet etc. On this basis, studies particularly contourlet transform and its features, proves that the family of contourlet basis functions in contourlet decomposition generated by biorthogonal filter bank makes a Riesz basis of L2 (R2), which further improves contourlet transform theory.
2. Puts forward a mammographic enhancement algorithm based on contourlet transform. Firstly by contourlet decomposition on mammographs with utilizing multiresolution and multidirection of contourlet transform, modifies the contourlet coefficient correspondingly; then by selecting proper threshold function and non-linear gain function, fulfils image denoising and enhancement, and displays explicitly the areas of interest such as microcalcification characterizing early breast cancer. Finally, utilizes evaluation indexes based on standard deviation to compare various enhancement algorithms in processing. Simulation shows that this enhancement algorithm has an apparent effect on mammographs, which not only offers convincing help and reference for diagnosis, but also lays a good foundation for other applications such as subsequent image segmentation and feature extraction.
3. Studies interpolation to diffusion tensor images. By analyzing the structural features of tensor images, presents two interpolation methods based on local gradient and linear structure tensor respectively with their fast implementation, where the former has a faster speed while the latter has a higher precision. Last, it is pointed out that image interpolation based on structure tensor is a generalized framework that contains an interpolation algorithm based on partial differential equation. Simulation shows that more precise results can be obtained by using these two interpolation algorithms compared with the traditional linear ones. Besides, structural features such as the raw signal edge can be reserved effectively, hence facilitating further research such as neural fasciculus tracing.
4. Studies effective image denoising algorithms. Further discusses nonlinear structure tensor based on linear structure tensor, presents a new image denoising algorithm by combing Wiener filtering, and its fast implementation by using AOC scheme, and simulations show thatbetter denoising effect is obtained by using the method.
Finally, sums up the work and research results, brings forward future research considerations and objects.
本文以医学图像处理过程中的实际需求为目标,侧重于医学图像处理的算法研究。通过利用多尺度几何分析方法、线性和非线性结构张量等新的数学工具,针对乳腺X线图像增强、扩散张量图像插值放大和图像去噪等问题进行了研究,结合医疗设备获取的图像的特点建立了合理的数学模型,并提出相应的求解算法和快速实现格式以改善图像质量,为有效诊断病情提供科学合理的依据和后续进一步研究奠定良好基础。本文的主要研究内容和创新点包括以下几个方面：
1.介绍了多尺度几何分析方法的基本理论,分析了后小波时代几种典型多尺度几何分析方法的优缺点。在此基础上着重研究了Contourlet变换及其性质,利用多方向多分辨分析理论证明了由双正交滤波器组生成的Contourlet分解中Contourlet基函数族构成了L2(R2)的Riesz基,进一步完善了Contourlet变换理论。
2.提出了基于Contourlet变换的乳腺X线图像增强算法。乳腺X线照相技术是针对无明显症状的乳腺癌患者进行早期检测的有效方法之一。不过,通常乳腺X线照片只能显示检测到的信息的3%,主要的原因在于正常腺状组织和恶性肿瘤之间的对比度过低。因此为了增强乳腺X线照片的对比度以提高图像的质量,首先通过利用Contourlet变换的多分辨多方向特性对乳腺X线照片进行Contourlet分解,再选取合适的阈值函数和非线性增益函数对Contourlet系数作相应的修改,完成图像去噪和增强,突出显示了表现为早期乳腺癌的微小钙化块等感兴趣的区域。最后,还利用基于标准偏差的评价指标比较了不同增强算法的处理效果。仿真实验表明,本文的算法增强的乳腺X线图像效果明显,不仅为医生诊断提供了有力的帮助和参考,而且为后续图像分割、特征提取等其它应用奠定了良好基础。
3.对扩散张量图像的插值放大进行了研究。为得到更加精细的张量场估计,结合张量图像自身的结构特性,给出了基于梯度特征和线性结构张量的两种各有优点的插值方法,前者速度快,后者精度更高,并研究了插值算法的快速实现格式。最后,文中还指出基于结构张量的图像插值是包含了一种基于偏微分方程插值算法的更广义的插值框架。仿真实验表明,与传统的线性插值相比,这两种插值算法得到的结果更加精细,有效地保留了原始信号的边缘等结构特征,有利于跟踪大脑纤维束等进一步的研究。
4.研究对图像有效去噪的算法。在线性结构张量的基础上,进一步讨论了非线性结构张量,并结合Wiener滤波给出了一种新的图像去噪算法及其快速实现,减少了噪声的影响。
最后,对整个论文的工作和研究成果进行了总结,讨论了一些有待于进一步研究的问题以及下一步的研究设想和目标。
As an interdisciplinary field with practical value and wide application prospect, medical images processing has attracted various specialized staff in electronics, mathematics and physics to participate in its research. Medical images are characterized by large information amount, fuzziness and processing complexity. Though, owing to the fast development and application of computer graphics and image processing, the imaging quality and displaying method have been greatly improved along with the accuracy and correctness of clinical diagnosis, which not only brings the effectiveness and potential of medical imaging equipment into full play, but also improves the diagnostic and therapeutic level as well as people's health. Hence, it's of great realistic and theoretical significance to research on medical images processing.
Aiming at the practical requirement in medical images processing, the dissertation focuses more on the study of algorithms. By such mathematical tools as multiscale geometric analysis and structure tensor, research is done on mammographic image enhancement, diffusion tensor image interpolation and image filtering, proper mathematical models are built based on the characteristics of the images acquired by the medical equipment, corresponding algorithm and fast implementation are presented to improve image quality, thus offering scientific references for effective diagnosis and a good foundation for further study. The main work and contributions of the dissertation are outlined as follows:
1. Introduces the basic theory of multiscale geometric analysis, analyses the advantages and disadvantages of several typical transforms, such as bandelet, curvelet etc. On this basis, studies particularly contourlet transform and its features, proves that the family of contourlet basis functions in contourlet decomposition generated by biorthogonal filter bank makes a Riesz basis of L2 (R2), which further improves contourlet transform theory.
2. Puts forward a mammographic enhancement algorithm based on contourlet transform. Firstly by contourlet decomposition on mammographs with utilizing multiresolution and multidirection of contourlet transform, modifies the contourlet coefficient correspondingly; then by selecting proper threshold function and non-linear gain function, fulfils image denoising and enhancement, and displays explicitly the areas of interest such as microcalcification characterizing early breast cancer. Finally, utilizes evaluation indexes based on standard deviation to compare various enhancement algorithms in processing. Simulation shows that this enhancement algorithm has an apparent effect on mammographs, which not only offers convincing help and reference for diagnosis, but also lays a good foundation for other applications such as subsequent image segmentation and feature extraction.
3. Studies interpolation to diffusion tensor images. By analyzing the structural features of tensor images, presents two interpolation methods based on local gradient and linear structure tensor respectively with their fast implementation, where the former has a faster speed while the latter has a higher precision. Last, it is pointed out that image interpolation based on structure tensor is a generalized framework that contains an interpolation algorithm based on partial differential equation. Simulation shows that more precise results can be obtained by using these two interpolation algorithms compared with the traditional linear ones. Besides, structural features such as the raw signal edge can be reserved effectively, hence facilitating further research such as neural fasciculus tracing.
4. Studies effective image denoising algorithms. Further discusses nonlinear structure tensor based on linear structure tensor, presents a new image denoising algorithm by combing Wiener filtering, and its fast implementation by using AOC scheme, and simulations show thatbetter denoising effect is obtained by using the method.
Finally, sums up the work and research results, brings forward future research considerations and objects.
引文
[1]王耀南,李树涛,毛建旭.计算机图像处理与识别技术[M].北京：高等教育出版社,2001.
[2]Gonzalez R C and Woods R E.阮秋琦,阮宇智等译.数字图像处理(第二版)[M].北京：电子工业出版社,2003.
[3]罗述谦,周果宏.医学图像处理与分析[M].北京：科学出版社,2003.
[4]赵于前.基于数学形态学的医学图像处理理论与方法研究[D].南京：中南大学,2006.
[5]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.
[6]谢静霞,杜湘珂主编.医学影像学[M].北京：北京医科大学出版社,2002.
[7]冯晓源.现代医学影像学进展[M].上海：复旦大学出版社,2005.
[8]唐孝威.核医学和放射治疗技术[M].北京：北京医科大学出版社,2001.
[9]陈映.金卫工程简介[EB/OL].二十一世纪中医药网络教育中心,2001-09-21/2007-08-19, http://gb.itcmedu.com/yyzy/tcmp/wltd3 r3.html.
[10]首都医科大学医学图像实验室.中国数字化虚拟人研究[EB/OL].中国数字化虚拟人,2001-12-06/2007-05-06, http://www.digitalhuman.net.cn/zonglan/china.htm.
[11]曾利明.中国百万妇女乳腺普查工程[N/OL].中国人口网,2005-04-22/2007-01-22,http://www.chinapop.gov.cn/kpyd/5ybj/t20050422_21683.htm.
[12]陶玲.基于医学断层图像的三维可视化实现技术研究[D].南京：南京航空航天大学,2005.
[13]Laine A, Fan J, and Yang W. Wavelets for contrast enhancement of digital mammography[J]. IEEE Engineering in Medicine and Biology Magazine,1995,11(5): 536-550.
[14]章毓晋.中国图像工程及当前的几个研究热点[J].计算机辅助设计与图形学学报,2002,14(6)：489-500.
[15]章毓晋.中国图象工程：2003[J].中国图象图形学报,2004,9(5)：513-531.
[16]章毓晋.中国图象工程：2004[J].中国图象图形学报,2005,10(5)：537-560.
[17]章毓晋.中国图象工程：2005[J].中国图象图形学报,2006,11(5)：601-623.
[18]章毓晋.中国图象工程：2006[J].中国图象图形学报,2007,12(5)：753-775.
[19]Kim M H, Lee Y B, Choi Y J. Advanced medical image visualization and analysis systems for diagnosis and treatment planning[A]. In Proceedings of 2004 International Symposium on Intelligent Signal Processing and Communication Systems[C],2004: 244-249.
[20]Duncan J S, Ayache N. Medical image analysis:progress over tewo decades and the challenges ahead[J]. IEEE Trans on PAMI,2000,22(1):85-106.
[21]程勇,陈卫国,牛艳坤.医学图像后处理技术研究进展[J].医疗卫生装备,2005,26(9)：22-24.
[22]Hummel R. Histogram modification techniques[J]. Computer Graphics and Image Processing,1975,4:209-224.
[23]Frei W. Image enhancement by histogram hyperbolization[J]. Computer Graphics and Image Processing,1977,6:286-294.
[24]Gorden R, Rangayyan R M. Feature enhancement of film mammograms using fixed and adaptive neighborhoods[J]. Applied Optics,1984,23:560.
[25]Dhawan A P, Buelloni G, Gorden R. Enhancement of mammographic features by optimal adaptive neighborhood image processing[J]. IEEE Trans on Medical Imaging, 1986, MI-5:8.
[26]Dhawan A P, Royer E. Mammographic feature enhancement by computerized image processing[J]. Computer Methods and Programs in Bimedicine,1988,27:23-30.
[27]Tahoces P G, Gorrea, Souto M, et al. Enhancement of chest and breast radiographs by automatic spatial filtering[J]. IEEE Trans on Medical Imaging,1991, MI-10(3), 330-335.
[28]Li H, Wang Y, Liu K J R, et al. Computerized radiographic mass detection-Part Ⅰ: Lesion site selection by morphological enhancement and contextual segmentation[J]. IEEE Trans on Medical Imaging,2001,20(4):289-301.
[29]Mukhopadhyay S, Chanda B. A multiscale morphological approach to local contrast enhancement[J]. Signal Processing,2000,80(4):685-696.
[30]Laine A, Song S. Multiscale wavelet representations for mammographic feature analysis[A]. In Proceeding of SPIE:Conference on Mathematical Methods in Medical Imaging[C], San Diego, CA,1992.
[31]Laine A, Schuler S, Fan J, Huda W. Mammographic feature enhancement by multiscale analysis[J]. IEEE Trans on Medical Imaging,1994,13(14):725-740.
[32]Koren I, Laine A and Taylor F. Enhancement via fusion of mammographic features[A]. In Proceedings on International Conference on Image Processing[C], Chicago, IL, USA,1998.
[33]Sakellaropoulos P, Costaridou L and Panayiotakis G. A wavelet-based spatially adaptive method for mammographic contrast enhancement[J]. Physics in Medicine and Biology,2003,48(6):787-803.
[34]Stefanou H, Kakouros S, Cavouras D, Wallace M. Wavelet-based mammographic enhancement[A]. In Proceedings of International Network Conference[C], Greece, 2005.
[35]烟玉芹,王光彬.伪彩色处理及其在医学上的应用[J].医学影像学杂志,2003, 13(1):878-879.
[36]Amit C, Lawrence S. Deformable boundary finding in medical images by integrating gradient and region information[J]. IEEE Trans on Medical Imaging,1996,15(6): 859-870.
[37]Jayarm U K, Supun S. Fuzzy connectedness and object definition:Theory, Algorithms, and Applications in image segmentation[J]. Models and Image Processing,1996,58(3): 246-252.
[38]Clark M C, Hall L O. Automatic tumor segmentation using knowledge-based technique[J]. IEEE Trans on Medical Imaging,1998,17(2):187-201.
[39]Jesse M. Segmentation of multidimensional magnetic using a fuzzy neural network[J]. In Proceedings of SPIE the International Society for Option Engineering, 1994,2298:636-643.
[40]Deschenes S, de Guise J. Wavelet-based automatic segmentation of the vertebral bodies in digital radiographs [A]. In Proceedings on ICASSP[C],2002:3868-3871.
[41]Zhang X P. Multiscale tumor detection and segmentation in mammograms[A]. In Proceedings of IEEE Biomedical Imaging[C],2002:213-216.
[42]Raquel V C, Veronica M B, Oscar Y S. Coupling of radial-basis network and active contour model for multispectral brain MRI segmentation[J]. IEEE Trans on Biomedical Engineering,2004,51(3):459-467.
[43]Luo X P, Tian J, Lin Y. A algorithm for segmentation of medical image series based on active contour model [J]. Journal of sofware,2002,13(6):1050-1057.
[44]Petra A, Elsen V. Medical image maching:A review with classification[J]. IEEE Trans Med Imag,1993,12(3):26-39.
[45]Frederik M, Andre C. Multimodality image registration by maximization of mutual information[J]. IEEE Trans Med Imag,1997,16(2):187-198.
[46]李坤成,刘景文,赵欣等.MRI与正电子发射体层摄影图像配准和融合技术在Alzhermer病诊断中的初步应用[J].中华放射学杂志,2001,35(1)70-73.
[47]卢健,胡志忠,杨如乃.医学图像融合技术的研究[J].上海生物医学工程,2006,27(3)： 163-167.
[48]Levoy M. Volume rendering:Display of surfaces from volume data[J]. IEEE Trans Computer Graphics Application,1988,8(3):29-37.
[49]Levoy M. Efficient ray tracing of volume data[J]. ACM Trans Graphics,1990,9(3): 245-261.
[50]Elvins T T. A survey of algorithms of volume visualization[J]. Computer Graphics, 1992,26(3):194-201.
[51]Salih B G, Carlo T, Bernd G. Medical image compression based on region of interest, with application to colon CT images[J]. Eng Med Biol Soc,2001,22:2453-2456.
[52]张敏,黄继武,戴宪华等.面向任务的医学图像压缩[J].中国图像图形学报,2001,(7)：675-680.
[53]Skodras A, Christopoulos C, and Ebrahimi T. The JPEG2000 still image compression standard[J]. IEEE Signal Processing Magazine,2001,18:36-58.
[54]焦李成,谭山,刘芳.脊波理论：从脊波变换到Curvelet变换[J].工程数学学报,2005,22(5)：761-773.
[55]焦李成,谭山.图像的多尺度几何分析：回顾和展望[J].电子学报,2003,31(12A)：1975-1981.
[56]Pennec E L, Mallat S. Image compression with geometrical wavelets[A]. In Proceeding of ICIP[C], Canada,2000,661-664.
[57]Candes E J. Curvelets[R]. USA:Department of Statistics Stanford University,1999.
[58]Do M N, Vetterli M. Contourlets[M]. New York:Academic Press,2003.
[59]Do M N and Vetterli M. The contourlet transform:an efficient directional multiresolution image representation[J]. IEEE Trans. on Image Processing,2005, 14(12):2091-2106.
[60]Candes E J. Harmonic analysis of neural network[J]. Applied and Computational Harmonic Analysis,1999,6:197-218.
[61]沈敏.Contourlet变换及其在图像处理中的应用研究[D].合肥：安徽大学,2006.
[62]Hubel D H and Wiesel T N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex[J]. Journal of Physiology,1962,160:106-154.
[63]Olshausen B A and Field D J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images [J]. Nature,1996:607-609.
[64]Brut P J and Adelon E H. The Laplacian pyramid as a compact image code[J]. IEEE Trans Commun,1983,31(4):532-540.
[65]Hough P V C. Methods and means for recogninzing complex patterns[P]. US Patent 3069654,1962.
[66]Do M N and Vetterli M. Framing pyramids[J]. IEEE Trans on Signal Processing,2003, 51:2329-2342.
[67]Bamberger R H and Smith M J T. A filter bank for the directional decomposition of images:theory and design[J]. IEEE Trans on Signal Processing,1992,40(4):882-893.
[68]Park S, Smith M J T, Mersereau R M. A new directional filter bank for image analysis and classification[A]. In Proc IEEE Int Conf Acoust Speech[C], and Signal Proc,1999: 1417-1420.
[69]Do M N. Directional multiresolution image representations[D]. Switzerland:Swiss Federal Institute of Technology,2001.
[70]Po D D-Y and Do M N. Directional multiscale modeling of images using the contourlet transform[J]. IEEE Trans on Image Processing,2006,15(6):1610-1620.
[71]Chui C K. Wavelets:A tutorial in theory and applications[M]. New York:Academic Press Inc,1992.
[72]张贤达.现代信号处理(第二版)[M].北京：清华大学出版社,2002,10.
[73]Duffin R J and Schaeffer A C. A class of nonharmonic Fourier series[M]. Trans Amer Math Soc,1952,72:341-366.
[74]Mallat S. A wavelet tour of signal processing(2nd)[M]. Beijing:China Machine Press, 2003.
[75]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.
[76]Cheng H D. Contrast enhancement based on a novel homogeneity measurement[J]. Pattern Recognition,2003,36:2687-2697.
[77]Mudigonda N R, Rangayyan R M and Desautels J E. Detection of breast masses in mammograms by density slicing and texture Flow-Field analysis[J]. IEEE Trans. on Medical Imaging,2001,20(12):1215-1227.
[78]Veldkamp W J H and Karssemeijer N. Normalization of local contrast in mammograms [J]. IEEE Trans. on Medical Imaging,2000,19(7):731-738.
[79]宋三泰.乳腺癌的现状及治疗[EB/OL].乳腺普查网,2004-10-22/2006-04-22. http://www.ladyhealth.org/detail.asp?aid=260.
[80]沈镇宙,邵志敏.现代乳腺肿瘤学进展[M].上海：上海科技文献出版社,2002.
[81]李坤成.乳腺影像诊断学[M].北京：人民卫生出版社,2003.
[82]沈镇宙.乳腺肿瘤学[M].上海：上海科学技术出版社,2005.
[83]李泉旺.乳腺X线照相术在乳腺癌防治中的作用[EB/OL].肿瘤网,2007-01-26/2007-04-05, http://www.orientaloncology.com/gengduo/20070126915.htm.
[84]章毓晋.图像工程(第二版)[M].北京：清华出版社,2007.
[85]Wang D C, Vagnucci A H, Li C C. Digital image enhancement:a survey[J]. Computer Vision, Graphics and Image Processing,1983,24:363-381.
[86]Rao D H. A Survey on Image Enhancement Techniques:Classical Spatial Filter, Neural Network, Cellular Neural Network, and Fuzzy Filter[A]. In Proceedings of IEEE International Conference on Industrial Technology[C], India,2006.
[87]曾明.基于知识和模型的计算机视觉若干关键问题研究[D].天津：南开大学,2006.
[88]Pizer S M, Zimmerman J B, Staab E V. Adaptive grey level assignment in CT scan display[J]. J Comput Assist Tomogr.,1984,8(2):300-305.
[89]Pizer S M, Amburn E P, Austin J D, et al. Adaptive histogram equalization and its variations [J]. Comuter Vision, Graphics, and Image Processing,1987,39(3):355-368.
[90]Kim J Y, Kim L S, Hwang S H. An advanced contrast enhancement using partially overlapped sub-block histogram equalization[J]. IEEE Trans on Circuits and Systems for Video Technology,2001,11(4):475-477.
[91]Caselles V, Lisani J L, Morel J M, et al. Shape preserving local histogram modification[J]. IEEE Trans on Image Processing,1999,8(2):220-230.
[92]Koenderink J J. The structure of image[J]. Biol Cybernet,1984,50:363-370.
[93]Perona P and Malik J. Scale space and edge detection using anisotropic diffusion[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1990, 12:629-639.
[94]Radmoser E, Scherzer O and Weickert J. Scale-space properties of nonstationery iterative regurlarzation method[J]. Journal of Visual Communication and Image Representation,2000,8:96-114.
[95]Gilboa G, Zeevi Y Y and Sochen N. Anisotropic selective inverse diffusion for signal enhancement in the presence of noise [A]. In IEEE International Conference on Acoustics, Speech and Signal Processing[C], Istanbul, Turkey,2000, I:221-224.
[96]Rudin L I, Osher S, and Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D,1992,60:259-268.
[97]Alvarez L and Mazorra L. Signal and image restoration using shock filters and anisotropic diffusion[J]. SIAM Numerical Analysis,1994,31(2):590-605.
[98]Sochen N, Kimmel R and Malldi R. A general framework for low level vision[J]. IEEE Trans on Image Processing,1998,7(3):310-318.
[99]Manay S, Yezzi A. Anti-geometric diffusion for adaptive thresholding and fast segmentation[J]. IEEE Trans on Image Processing,2003,12(11):1310-1323.
[100]李兰兰.基于偏微分方程的图像复原和增强算法研究[D].南京：东南大学,2005.
[101]Sattar F, Floreby L, Salomonsson G, et al. Image enhancement based on a nonlinear multiscale method[J]. IEEE Trans on Image Processing,1997,6(6):888-895.
[102]Jung C R, Scharcanski J. Adaptive image denoising and edge enhancement in scale-space using the wavelet transform[J]. Pattern Recognition Letters,2003,24(7):965-971.
[103]吴颖谦,施鹏飞.基于小波变换的低对比度图像增强[J].红外与激光工程,2003,32(1)：4-7.
[104]翟广涛,王欣.基于多尺度边缘表示的图像增强快速算法[J].中国图像图形学报,2004,9(12)：1454-1458.
[105]唐志航,黄哲,张东衡,等.基于尺度相关性的自适应图像增强新算法[J].计算机应用,2006,26(9)：2084-2088.
[106]周旋,周树道,黄峰,周小滔.基于小波变换的图像增强新算法[J].计算机应用,2005,25(3)：606-608.
[107]Kang H K, Ro Y M and K S M. A microcalcification detection using adaptive contrast enhancement on wavelet transform and neural network[J]. IEICE Trans on Information and Systems,2006, E89-D(3):1280-1287.
[108]Cheng H D, Cai X, Chen X, et al. Computer-aided detection and classification of microcalcifications in mammograms:a survey[J]. Pattern Recognition,2003,36(12): 2967-2991.
[109]孙蕾.医学图像智能挖掘关键技术研究[D].西安：西北大学,2005.
[110]叶裕雷,戴文战.一种基于新阈值函数的小波信号去噪方法[J].计算机应用,2006,26(7)： 1617-1619.
[111]Yan Y and Osadciw L. Contourlet based image recovery and de-noising through wireless fading channels[A]. In 2005 Conference on Information Science and Systems[C], Maryland, USA, March 2005.
[112]Donoho D L. De-noising by soft-thresholding[J]. IEEE Trans on Information Theory, 1995,41(3):617-627.
[113]Gonzalez R C, Woods R E, and Eddins S L. Images from Digital image processing using matlab[DB/OL].2002-01-07/2004-04-01, http://www.imageprocessingplace.com/ root_files_V3/image_databases.htm.
[114]Morrow W M, Parajape R B, Rangayyan R M. Region-based contrast enhancement of mammograms[J]. IEEE Trans Med Imag,1992,11(3):392-406.
[115]Kim J K, Park J M, Song K S, Park H W. Adaptive mammographic image enhancement using first derivative and local statistics[J]. IEEE Trans Med Imaging,1997,16(5): 495-502.
[116]Bovis K and Singh S. Enhancement technique evaluation using quantitative measures on digital mammograms[A]. In Proceeding of the 5th International Workshop on Digital Mammography[C], Toronto, Canada,2000:547-553.
[117]Suckling J, Parker J, Dance D et al. The mammographic images analysis society digital mammogram database[J], Exerpta Medica, International Congress Series,1069: 375-378.
[118]Meijering E. A chronology of interpolation:From ancient astronomy to modern signal and image processing[J]. Proceedings of the IEEE,2002,90(3):319-342.
[119]Lehmann T M, Gonner C and Spitzer K. Survey:Interpolation methods in medical image processing[J]. IEEE Trans on Medical Image,1999,18(11):1049-1075.
[120]Taylor W D, Hsu E, Krishnan K R, MacFall J R. Diffusion Tensor Imaging: Background, Potential, and Utility in Psychiatric Research[J]. Biol. Psychiatry,2004, 55:201-207.
[121]Jiang H, Moloney C. A new direction adaptive scheme for image interpolation[A]. In Processing of International Conference on Image Processing[C].2002:369-372.
[122]韩鸿宾.临床磁共振成像序列设计与应用[M].北京：北京大学医学出版社,2003.
[123]朱礼涛.核磁共振脑图像的分割和形状分析[D].北京：中科院自动化所,2004.
[124]江贵平.磁共振运动伪影消除与扩散张量成像技术研究[D].广州：南方医科大学,2005.
[125]Tuch D. Diffusion MRI of complex tissue structure[D]. Massachusetts Institute of Technology,2002.
[126]Le Bihan D, Breton E. Imagerie de diffusion in vivo par resonance magnetique nuclearire[J]. CR Acad Sci Paris,1985,301:1109-1112.
[127]Basser P J, Mattiello J, LeBihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo[J]. Journal of Magnetic Resonance B,1994,103:247-254.
[128]Le Bihan D. Molecular diffusion nuclear magnetic resonance imaging[J]. Magn Reson Q,1991,7(1):1-30.
[129]林富春.基于扩散张量成像的模式分类、纤维束分析方法及应用[D].北京：中科院自动化所,2006.
[130]Basser P J, Pajevic S, Pierpaoli C, et al. In vivo fiber tractography using DT-MRI data[J]. Magn Reson Med,2000,44(4):625-632.
[131]Parker G J, Haroon H A, Wheeler-Kingshott C A. A framework for a streamline-based probabilistic index of connectivity using a structural interpretation of MRI diffusion measurements [J]. J Magn Reson Imaging,2003,18(2):242-254.
[132]Westin C F, Maier S E, Mamata H, et al. Processing and visualization for diffusion tensor MRI[J]. Med Image Anal,2002,6(2):93-108.
[133]Tschumperle D, Deriche R. Variational frameworks for DT-MRI estimation, regularization and visualization[A]. In Proceedings of ICCV [C],2003:116-121.
[134]Coulon O, Alexander D C, Arridge S. Diffusion tensor magnetic resonance image regularization[J]. Med Image Anal,2004,8(1):47-67.
[135]Tschumperle D, Deriche R. Diffusion tensor regularization with constraints preservation[A]. In Proceedings of CVPR[C],2001:948-953.
[136]Xu D, Mori S, Shen D, et al. Spatial normalization of diffusion tensor fields[J]. Magn Reson Med,2003,50(1):175-182.
[137]Sullivan E V, Pfefferbaum A. Diffusion tensor imaging in normal aging and neuropsychiatric disorders [J]. Eur J Radiol,2003,45(3):244-255.
[138]Bankman I N. Handbook of medical imaging, processing and analysis[M]. New York: Academic,2000:393-420.
[139]Ostuni J L, Santha A K S, Mattay V S, et al. Analysis of interpolation effects in the reslicing of functional MR images[J]. Journal of Computer Assisted Tomography, 1997,21:803-810.
[140]Moller T, Machiraju R, Mueller K and Yagel R. Evaluation and design of filters using a Taylor series expansion[J]. IEEE Trans on Visualization and Computer Graphics, 1997,3:184-199.
[141]Ruttimann U E, Andreason P J and Rio D. Head motion during positron emission tomography:is it significant? [J]. Psychiatry Research:Neuroimaging,1995,61: 43-51.
[142]Rowland S W. Computer implementation of image reconstruction formulas[A]. In Image Reconstruction from Projections:Implementation and Applications[C]. Berlin, Germany:Springer-Verlag,1979.
[143]Grevera G J and Udupa J K. An objective comparison of 3D image interpolation methods[J]. IEEE Trans Med Image,1998,17(4):642-652.
[144]李将云.图像处理中的插值和缩放若干技术研究[D].杭州：浙江大学,2002.
[145]Unser M, Aldroubi A, and Eden M. Fast B-splines transforms for continuous image representation and interpolation[J]. IEEE Trans Pattern Anal Image Processing,1991, 13:277-285.
[146]Unser M, Thevenaz P, and Yaroslavsky L. Convolution-based interpolation for fast, high-quality rotation of images[J]. IEEE Trans Image Processing,1995,4:1371-1381.
[147]Blu T, Thevenaz P, Unser M. Linear interpolation revitalized[J]. IEEE Transactions on Image Processing,2004,13(5):710-719.
[148]Goshtasby A, Truner D A and Ackerman L V. Matching of Tomographic slices for interpolation[J]. IEEE Trans Med Img,1992,11(4):507-516.
[149]Higgins W E, Orlick C J and Ledell B E. Nonlinear filtering approach to 3-D gray-scale image interpolation[J]. IEEE Trans Med Imag,1996,15(4):580-587.
[150]Tam R C and Fournier A. Image Interpolation using unions of spheres[J]. Visual Computer,1998,14:401-414.
[151]Ballester C,Bertalmio M, Caselles V et al. Filling-in by joint interpolation of vector fields and gray levels[J]. IEEE Trans Image Proc,2001,10(8):1200-1211.
[152]Gao Q and Yin F F. Two-Dimensional refinement scheme of direction-based interpolation with discrete orthogonal polynomial decomposition[J]. SPIE,1997,3031: 12-18.
[153]Plaziac N. Image Interpolation Using Neural Networks[J]. IEEE Trans Image Proc, 1999,8(11):1647-1217.
[154]Morse B S, Schwartzwald D. Isophote-based interpolation[A]. In IEEE Processing of International Conference on Image Processing[C].1998:227-231.
[155]Li X. Orchard MT. New edge-directed interpolation[J]. IEEE Transactions on Image Processing,2001,10:1521-1527.
[156]Lu X, Hong P S, Smith M J. An efficient directional image interpolation method[A]. In Processing of IEEE International Conference on Acoustics, Speech, and Signal[C], 2003:97-100.
[157]Carrato S, Tenze L. A high quality 2 X image interpolator[J]. IEEE Signal Processing Letters,2000,7:132-134.
[158]谢美华,王正明.基于图像梯度信息的插值方法[J].中国图像图形学报,2005,10(7)： 856-861.
[159]Thevenaz P, Blu T and Unser M. Interpolation revisited [J]. IEEE Trans on Medical Imaging,2000,19(7):739-758.
[160]Pajevic S, Aldroubi A, Basser P J. A continuous tensor field approximation of discrete DT-MRI data for extracting microstructural and architectural features of tissue[J]. Journal of Magnetic Resonance,2002,154:85-100.
[161]Castano-Moraga C A, Rodriguez-Florido M A, Alvarez L, Westin C F, Ruiz-Alzola, J. Anisotropic Interpolation of DT-MRI[A]. In Proceedings of the 7th International Conference on Medical Image Computing and Computer-Assisted Intervention [C], Saint-Malo, France,2004:343-350.
[162]Thurnhofer S and Mitra S. Edge-enhanced image zooming[J]. Optical Engineering, 1996,35(7):1862-1870.
[163]Schaum A. Theory and design of local interpolators [J]. CVGIP:Graph Models Image Processing,1993,55(6):464-481.
[164]HauBecker H, Jahne B. A tensor approach for local structure analysis in multi-dimensional images [A]. In Proceedings 3D Image Analysis and Synthesis [C], Universitat Erlangen-Nurnberg,1996.
[165]Knutsson H. Representing local structure using tensors [A]. In 6th Scandinavian Conference on Image Analysis[C], Oulu, Finland,1989:244-251.
[166]Knutsson H, and Andersson M. What's so good about quadrature filters? [A]. In 6th 2003 IEEE International Conference on Image Processing[C],2003(111):61-64.
[167]Bigun J and Granlund G H. Optimal orientation detection of linear symmetry[A]. In IEEE First International Conference on Computer Vision[C], London,1987,433-438.
[168]Jahne B. Spatio-Temporal Image Processing, Theory and Applications[M]. LNCS, vol 751, Springer, Berlin,1993.
[169]Jahne B. Digital Image Processing:First-order Tensor Representation[M]. Springer-Verlag Berlin Heidelberg,1997.
[170]Theuβl T, Hauser H, Groller E. Mastering windows:Improving reconstruction[A]. In Proceedings of IEEE Volume Visualization[C],2000:101-108.
[171]Carey W K, Chung D B and Hemami S S. Regularity-preserving image interpolation[J]. IEEE Trans Image Processing,1999,8:1293-1297.
[172]Munoz A, Blu T and Unser M. Least-squares image resizing using finite difference[J]. IEEE Trans Image Processing,2001,10:1365-1378.
[173]王欣,王德隽.离散信号的滤波[M].北京：电子工业出版社,2002,6.
[174]邹谋炎.反卷积和信号复原[M].北京：国防工业出版社,2001.
[175]Aubert G and Kornprobst P. Mathematical problems in image processing-Partial differential equations and the calculus of variations [M].北京：世界图书出版公司北京公司,2005.
[176]Grafakos L. Classical and modern Fourier analysis[M]. Beijing:China Machine Press, 2006.
[177]Joham M and Zoltowski M D. Interpretation of the Multi-Stage Nested Wiener Filter in the Krylov Subspace Framework[R]. Technical Report TUM-LNS-TR-00-6, Munich University of Technology, November,2000.
[178]Lim JS. Two-Dimensional Signal and Image Processing[M], Prentice-Hall,1990.
[179]Lee J S. Digital image enhancement and noise filtering by use of local statistics[J]. IEEE Trans Pattern Analysis and Machine Intelligence,1980,2(2): 165-168.
[180]尚晓清,王军锋,宋国乡.基于Bayesian估计和Wiener滤波的阈值去噪方法[J].光子学报,2003,32(7)：889-891.
[181]赵艳明,全子一.一种有效的小波-Wiener滤波去噪算法[J].北京邮电大学学报,2004,27(4)：41-45.
[182]闫华,刘琚,李道真,孙建德.基于增量维纳滤波和空间自适应规整的超分辨率图像复原[J].电子与信息学报,2005,27(1)：35-38.
[183]侯建华,田金文,柳健.一种迭代小波域维纳滤波算法[J].华中科技大学学报：自然科学版,2006,34(4)：24-26.
[184]侯建华,田金文,柳健.一种小波域与空域相结合的图像滤波方法[J].红外与激光工程,2006,35(1)：122-126.
[185]Das M. An improved adaptive wiener filter for de-noising and signal detection[A]. In Proceedings of the IASTED International Conference Signal and Image Processing[C], Honolulu, USA, August,2005:226-230.
[186]Bigun J and Granlund G H. Optimal orientation detection of linear symmetry[A]. In proceeding 1st IEEE International Conference on Computer Vision[C], London, England, June,1987:433-438.
[187]Bigun J and Granlund G H, and Wiklund J. Multidimensional orientation estimation with applications to texture analysis and optical flow[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1991,13(8):775-790.
[188]Thomas I. Anisotropic adaptation and structure detection[R]. Technical Report F11, Institute for Applied Mathematics, University of Hamburg, Germany, Aug,1999.
[189]Nagel H H and Gehrke A. Spatiotemporally adaptive estimation and segmentation of OF-field[A]. In 5th European Conference on Computer Vision[C], volume 1407 of Lecture Notes in Computer Science, Springer, Freiburg, Germany,1998:86-102.
[190]Middendorf M and Nagel H. H. Empirically convergent adaptive estimation of grayvalue structure tensors[A]. In Pattern Recognition[C], volume 2449 of Lecture Notes in Computer Science, Springer, Berlin,2002:66-74.
[191]Van Den Boomgaard R and van de Weijer J. Robust estimation of orientation for texture analysis[A]. In Proceeding. Second International Workshop on Texture Analysis and Synthesis[C], Copenhagen, June 2002.
[192]Kothe U. Edge and junction detection with an improved structure tensor[A]. In Pattern Recognition[C], volume 2781 of Lecture Notes in Computer Science, Springer, Berlin, 2003:25-32.
[193]Brox T, Weickert J, Burgeth B, and Mrazek P. Nonlinear structure tensor[R]. Technique Report 113, Department of Mathematics, Saarland University, Germany, October,2004.
[194]Weickert J. Anisotropic diffusion in image processing[M]. Teubner, Stuttgart,1998.
[195]Tshumperle D and Deriche R. Orthonormal vector sets regularization with PDE's and applications [J]. International Journal of Computer Vision,2002,50(3):237-252.
[196]Weickert J and Brox T. Diffusion and regularization of vector- and matrix-valued images[A]. In Inverse Problems, Image Analysis, and Medical Imaging[C], volume 313 of Contemporary Mathematics, AMS, Providence,2002:251-268.
[197]Gerig G, Kubler O, Kikinis R and Jolesz F A. Nonlinear anisotropic filtering of MRI data[J]. IEEE Transactions on Medical Imaging,1992,11:221-232.
[198]Charbonnier P, Blanc-Feraud L, Aubert G, and Barlaud M. Deterministic edge-preserving regularization in computed imaging[J]. IEEE transactions on image processing,1997,6(2):298-311.
[199]Dibos F and Koepfler G. Global total variation minimization[J]. SIAM Journal on Numerical Analysis,2000,33(2):646-664.
[200]Andreu F, Ballester C, Caselles B, and Mazon J. Minimizing total variation flow[J]. Differential and Integral Equations,2001,14:321-360.
[201]Andreu F, Ballester C, Caselles B, and Mazon J. Qualitative properties of the total variation flow[J]. Journal of Functional Analysis,2002,188(2):516-547.
[202]Bellettini G, Caselles V, and Novaga M. The total variation flow in RN[J]. Journal of Differential Equations,2002,184(2):475-525.
[203]Catte F, Lions P-L, Morel J-M, Coll T. Image selective smoothing and edge detection by nonlinear diffusion[J]. SIAM Journal of Numerical Analysis,1992,29:182-193.
[204]Weickert J. Recursive separable schemes for nonlinear diffusion filters[A]. Scale-space theory in computer vision[C], LNCS 1252, Springer, Berlin,1997:260-271.
[205]程建刚,田捷,何余良,杨鑫.基于非线性扩散滤波的指纹增强算法[J].自动化学报,2004,30(6)：854-862.
[2]Gonzalez R C and Woods R E.阮秋琦,阮宇智等译.数字图像处理(第二版)[M].北京：电子工业出版社,2003.
[3]罗述谦,周果宏.医学图像处理与分析[M].北京：科学出版社,2003.
[4]赵于前.基于数学形态学的医学图像处理理论与方法研究[D].南京：中南大学,2006.
[5]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.
[6]谢静霞,杜湘珂主编.医学影像学[M].北京：北京医科大学出版社,2002.
[7]冯晓源.现代医学影像学进展[M].上海：复旦大学出版社,2005.
[8]唐孝威.核医学和放射治疗技术[M].北京：北京医科大学出版社,2001.
[9]陈映.金卫工程简介[EB/OL].二十一世纪中医药网络教育中心,2001-09-21/2007-08-19, http://gb.itcmedu.com/yyzy/tcmp/wltd3 r3.html.
[10]首都医科大学医学图像实验室.中国数字化虚拟人研究[EB/OL].中国数字化虚拟人,2001-12-06/2007-05-06, http://www.digitalhuman.net.cn/zonglan/china.htm.
[11]曾利明.中国百万妇女乳腺普查工程[N/OL].中国人口网,2005-04-22/2007-01-22,http://www.chinapop.gov.cn/kpyd/5ybj/t20050422_21683.htm.
[12]陶玲.基于医学断层图像的三维可视化实现技术研究[D].南京：南京航空航天大学,2005.
[13]Laine A, Fan J, and Yang W. Wavelets for contrast enhancement of digital mammography[J]. IEEE Engineering in Medicine and Biology Magazine,1995,11(5): 536-550.
[14]章毓晋.中国图像工程及当前的几个研究热点[J].计算机辅助设计与图形学学报,2002,14(6)：489-500.
[15]章毓晋.中国图象工程：2003[J].中国图象图形学报,2004,9(5)：513-531.
[16]章毓晋.中国图象工程：2004[J].中国图象图形学报,2005,10(5)：537-560.
[17]章毓晋.中国图象工程：2005[J].中国图象图形学报,2006,11(5)：601-623.
[18]章毓晋.中国图象工程：2006[J].中国图象图形学报,2007,12(5)：753-775.
[19]Kim M H, Lee Y B, Choi Y J. Advanced medical image visualization and analysis systems for diagnosis and treatment planning[A]. In Proceedings of 2004 International Symposium on Intelligent Signal Processing and Communication Systems[C],2004: 244-249.
[20]Duncan J S, Ayache N. Medical image analysis:progress over tewo decades and the challenges ahead[J]. IEEE Trans on PAMI,2000,22(1):85-106.
[21]程勇,陈卫国,牛艳坤.医学图像后处理技术研究进展[J].医疗卫生装备,2005,26(9)：22-24.
[22]Hummel R. Histogram modification techniques[J]. Computer Graphics and Image Processing,1975,4:209-224.
[23]Frei W. Image enhancement by histogram hyperbolization[J]. Computer Graphics and Image Processing,1977,6:286-294.
[24]Gorden R, Rangayyan R M. Feature enhancement of film mammograms using fixed and adaptive neighborhoods[J]. Applied Optics,1984,23:560.
[25]Dhawan A P, Buelloni G, Gorden R. Enhancement of mammographic features by optimal adaptive neighborhood image processing[J]. IEEE Trans on Medical Imaging, 1986, MI-5:8.
[26]Dhawan A P, Royer E. Mammographic feature enhancement by computerized image processing[J]. Computer Methods and Programs in Bimedicine,1988,27:23-30.
[27]Tahoces P G, Gorrea, Souto M, et al. Enhancement of chest and breast radiographs by automatic spatial filtering[J]. IEEE Trans on Medical Imaging,1991, MI-10(3), 330-335.
[28]Li H, Wang Y, Liu K J R, et al. Computerized radiographic mass detection-Part Ⅰ: Lesion site selection by morphological enhancement and contextual segmentation[J]. IEEE Trans on Medical Imaging,2001,20(4):289-301.
[29]Mukhopadhyay S, Chanda B. A multiscale morphological approach to local contrast enhancement[J]. Signal Processing,2000,80(4):685-696.
[30]Laine A, Song S. Multiscale wavelet representations for mammographic feature analysis[A]. In Proceeding of SPIE:Conference on Mathematical Methods in Medical Imaging[C], San Diego, CA,1992.
[31]Laine A, Schuler S, Fan J, Huda W. Mammographic feature enhancement by multiscale analysis[J]. IEEE Trans on Medical Imaging,1994,13(14):725-740.
[32]Koren I, Laine A and Taylor F. Enhancement via fusion of mammographic features[A]. In Proceedings on International Conference on Image Processing[C], Chicago, IL, USA,1998.
[33]Sakellaropoulos P, Costaridou L and Panayiotakis G. A wavelet-based spatially adaptive method for mammographic contrast enhancement[J]. Physics in Medicine and Biology,2003,48(6):787-803.
[34]Stefanou H, Kakouros S, Cavouras D, Wallace M. Wavelet-based mammographic enhancement[A]. In Proceedings of International Network Conference[C], Greece, 2005.
[35]烟玉芹,王光彬.伪彩色处理及其在医学上的应用[J].医学影像学杂志,2003, 13(1):878-879.
[36]Amit C, Lawrence S. Deformable boundary finding in medical images by integrating gradient and region information[J]. IEEE Trans on Medical Imaging,1996,15(6): 859-870.
[37]Jayarm U K, Supun S. Fuzzy connectedness and object definition:Theory, Algorithms, and Applications in image segmentation[J]. Models and Image Processing,1996,58(3): 246-252.
[38]Clark M C, Hall L O. Automatic tumor segmentation using knowledge-based technique[J]. IEEE Trans on Medical Imaging,1998,17(2):187-201.
[39]Jesse M. Segmentation of multidimensional magnetic using a fuzzy neural network[J]. In Proceedings of SPIE the International Society for Option Engineering, 1994,2298:636-643.
[40]Deschenes S, de Guise J. Wavelet-based automatic segmentation of the vertebral bodies in digital radiographs [A]. In Proceedings on ICASSP[C],2002:3868-3871.
[41]Zhang X P. Multiscale tumor detection and segmentation in mammograms[A]. In Proceedings of IEEE Biomedical Imaging[C],2002:213-216.
[42]Raquel V C, Veronica M B, Oscar Y S. Coupling of radial-basis network and active contour model for multispectral brain MRI segmentation[J]. IEEE Trans on Biomedical Engineering,2004,51(3):459-467.
[43]Luo X P, Tian J, Lin Y. A algorithm for segmentation of medical image series based on active contour model [J]. Journal of sofware,2002,13(6):1050-1057.
[44]Petra A, Elsen V. Medical image maching:A review with classification[J]. IEEE Trans Med Imag,1993,12(3):26-39.
[45]Frederik M, Andre C. Multimodality image registration by maximization of mutual information[J]. IEEE Trans Med Imag,1997,16(2):187-198.
[46]李坤成,刘景文,赵欣等.MRI与正电子发射体层摄影图像配准和融合技术在Alzhermer病诊断中的初步应用[J].中华放射学杂志,2001,35(1)70-73.
[47]卢健,胡志忠,杨如乃.医学图像融合技术的研究[J].上海生物医学工程,2006,27(3)： 163-167.
[48]Levoy M. Volume rendering:Display of surfaces from volume data[J]. IEEE Trans Computer Graphics Application,1988,8(3):29-37.
[49]Levoy M. Efficient ray tracing of volume data[J]. ACM Trans Graphics,1990,9(3): 245-261.
[50]Elvins T T. A survey of algorithms of volume visualization[J]. Computer Graphics, 1992,26(3):194-201.
[51]Salih B G, Carlo T, Bernd G. Medical image compression based on region of interest, with application to colon CT images[J]. Eng Med Biol Soc,2001,22:2453-2456.
[52]张敏,黄继武,戴宪华等.面向任务的医学图像压缩[J].中国图像图形学报,2001,(7)：675-680.
[53]Skodras A, Christopoulos C, and Ebrahimi T. The JPEG2000 still image compression standard[J]. IEEE Signal Processing Magazine,2001,18:36-58.
[54]焦李成,谭山,刘芳.脊波理论：从脊波变换到Curvelet变换[J].工程数学学报,2005,22(5)：761-773.
[55]焦李成,谭山.图像的多尺度几何分析：回顾和展望[J].电子学报,2003,31(12A)：1975-1981.
[56]Pennec E L, Mallat S. Image compression with geometrical wavelets[A]. In Proceeding of ICIP[C], Canada,2000,661-664.
[57]Candes E J. Curvelets[R]. USA:Department of Statistics Stanford University,1999.
[58]Do M N, Vetterli M. Contourlets[M]. New York:Academic Press,2003.
[59]Do M N and Vetterli M. The contourlet transform:an efficient directional multiresolution image representation[J]. IEEE Trans. on Image Processing,2005, 14(12):2091-2106.
[60]Candes E J. Harmonic analysis of neural network[J]. Applied and Computational Harmonic Analysis,1999,6:197-218.
[61]沈敏.Contourlet变换及其在图像处理中的应用研究[D].合肥：安徽大学,2006.
[62]Hubel D H and Wiesel T N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex[J]. Journal of Physiology,1962,160:106-154.
[63]Olshausen B A and Field D J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images [J]. Nature,1996:607-609.
[64]Brut P J and Adelon E H. The Laplacian pyramid as a compact image code[J]. IEEE Trans Commun,1983,31(4):532-540.
[65]Hough P V C. Methods and means for recogninzing complex patterns[P]. US Patent 3069654,1962.
[66]Do M N and Vetterli M. Framing pyramids[J]. IEEE Trans on Signal Processing,2003, 51:2329-2342.
[67]Bamberger R H and Smith M J T. A filter bank for the directional decomposition of images:theory and design[J]. IEEE Trans on Signal Processing,1992,40(4):882-893.
[68]Park S, Smith M J T, Mersereau R M. A new directional filter bank for image analysis and classification[A]. In Proc IEEE Int Conf Acoust Speech[C], and Signal Proc,1999: 1417-1420.
[69]Do M N. Directional multiresolution image representations[D]. Switzerland:Swiss Federal Institute of Technology,2001.
[70]Po D D-Y and Do M N. Directional multiscale modeling of images using the contourlet transform[J]. IEEE Trans on Image Processing,2006,15(6):1610-1620.
[71]Chui C K. Wavelets:A tutorial in theory and applications[M]. New York:Academic Press Inc,1992.
[72]张贤达.现代信号处理(第二版)[M].北京：清华大学出版社,2002,10.
[73]Duffin R J and Schaeffer A C. A class of nonharmonic Fourier series[M]. Trans Amer Math Soc,1952,72:341-366.
[74]Mallat S. A wavelet tour of signal processing(2nd)[M]. Beijing:China Machine Press, 2003.
[75]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.
[76]Cheng H D. Contrast enhancement based on a novel homogeneity measurement[J]. Pattern Recognition,2003,36:2687-2697.
[77]Mudigonda N R, Rangayyan R M and Desautels J E. Detection of breast masses in mammograms by density slicing and texture Flow-Field analysis[J]. IEEE Trans. on Medical Imaging,2001,20(12):1215-1227.
[78]Veldkamp W J H and Karssemeijer N. Normalization of local contrast in mammograms [J]. IEEE Trans. on Medical Imaging,2000,19(7):731-738.
[79]宋三泰.乳腺癌的现状及治疗[EB/OL].乳腺普查网,2004-10-22/2006-04-22. http://www.ladyhealth.org/detail.asp?aid=260.
[80]沈镇宙,邵志敏.现代乳腺肿瘤学进展[M].上海：上海科技文献出版社,2002.
[81]李坤成.乳腺影像诊断学[M].北京：人民卫生出版社,2003.
[82]沈镇宙.乳腺肿瘤学[M].上海：上海科学技术出版社,2005.
[83]李泉旺.乳腺X线照相术在乳腺癌防治中的作用[EB/OL].肿瘤网,2007-01-26/2007-04-05, http://www.orientaloncology.com/gengduo/20070126915.htm.
[84]章毓晋.图像工程(第二版)[M].北京：清华出版社,2007.
[85]Wang D C, Vagnucci A H, Li C C. Digital image enhancement:a survey[J]. Computer Vision, Graphics and Image Processing,1983,24:363-381.
[86]Rao D H. A Survey on Image Enhancement Techniques:Classical Spatial Filter, Neural Network, Cellular Neural Network, and Fuzzy Filter[A]. In Proceedings of IEEE International Conference on Industrial Technology[C], India,2006.
[87]曾明.基于知识和模型的计算机视觉若干关键问题研究[D].天津：南开大学,2006.
[88]Pizer S M, Zimmerman J B, Staab E V. Adaptive grey level assignment in CT scan display[J]. J Comput Assist Tomogr.,1984,8(2):300-305.
[89]Pizer S M, Amburn E P, Austin J D, et al. Adaptive histogram equalization and its variations [J]. Comuter Vision, Graphics, and Image Processing,1987,39(3):355-368.
[90]Kim J Y, Kim L S, Hwang S H. An advanced contrast enhancement using partially overlapped sub-block histogram equalization[J]. IEEE Trans on Circuits and Systems for Video Technology,2001,11(4):475-477.
[91]Caselles V, Lisani J L, Morel J M, et al. Shape preserving local histogram modification[J]. IEEE Trans on Image Processing,1999,8(2):220-230.
[92]Koenderink J J. The structure of image[J]. Biol Cybernet,1984,50:363-370.
[93]Perona P and Malik J. Scale space and edge detection using anisotropic diffusion[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1990, 12:629-639.
[94]Radmoser E, Scherzer O and Weickert J. Scale-space properties of nonstationery iterative regurlarzation method[J]. Journal of Visual Communication and Image Representation,2000,8:96-114.
[95]Gilboa G, Zeevi Y Y and Sochen N. Anisotropic selective inverse diffusion for signal enhancement in the presence of noise [A]. In IEEE International Conference on Acoustics, Speech and Signal Processing[C], Istanbul, Turkey,2000, I:221-224.
[96]Rudin L I, Osher S, and Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D,1992,60:259-268.
[97]Alvarez L and Mazorra L. Signal and image restoration using shock filters and anisotropic diffusion[J]. SIAM Numerical Analysis,1994,31(2):590-605.
[98]Sochen N, Kimmel R and Malldi R. A general framework for low level vision[J]. IEEE Trans on Image Processing,1998,7(3):310-318.
[99]Manay S, Yezzi A. Anti-geometric diffusion for adaptive thresholding and fast segmentation[J]. IEEE Trans on Image Processing,2003,12(11):1310-1323.
[100]李兰兰.基于偏微分方程的图像复原和增强算法研究[D].南京：东南大学,2005.
[101]Sattar F, Floreby L, Salomonsson G, et al. Image enhancement based on a nonlinear multiscale method[J]. IEEE Trans on Image Processing,1997,6(6):888-895.
[102]Jung C R, Scharcanski J. Adaptive image denoising and edge enhancement in scale-space using the wavelet transform[J]. Pattern Recognition Letters,2003,24(7):965-971.
[103]吴颖谦,施鹏飞.基于小波变换的低对比度图像增强[J].红外与激光工程,2003,32(1)：4-7.
[104]翟广涛,王欣.基于多尺度边缘表示的图像增强快速算法[J].中国图像图形学报,2004,9(12)：1454-1458.
[105]唐志航,黄哲,张东衡,等.基于尺度相关性的自适应图像增强新算法[J].计算机应用,2006,26(9)：2084-2088.
[106]周旋,周树道,黄峰,周小滔.基于小波变换的图像增强新算法[J].计算机应用,2005,25(3)：606-608.
[107]Kang H K, Ro Y M and K S M. A microcalcification detection using adaptive contrast enhancement on wavelet transform and neural network[J]. IEICE Trans on Information and Systems,2006, E89-D(3):1280-1287.
[108]Cheng H D, Cai X, Chen X, et al. Computer-aided detection and classification of microcalcifications in mammograms:a survey[J]. Pattern Recognition,2003,36(12): 2967-2991.
[109]孙蕾.医学图像智能挖掘关键技术研究[D].西安：西北大学,2005.
[110]叶裕雷,戴文战.一种基于新阈值函数的小波信号去噪方法[J].计算机应用,2006,26(7)： 1617-1619.
[111]Yan Y and Osadciw L. Contourlet based image recovery and de-noising through wireless fading channels[A]. In 2005 Conference on Information Science and Systems[C], Maryland, USA, March 2005.
[112]Donoho D L. De-noising by soft-thresholding[J]. IEEE Trans on Information Theory, 1995,41(3):617-627.
[113]Gonzalez R C, Woods R E, and Eddins S L. Images from Digital image processing using matlab[DB/OL].2002-01-07/2004-04-01, http://www.imageprocessingplace.com/ root_files_V3/image_databases.htm.
[114]Morrow W M, Parajape R B, Rangayyan R M. Region-based contrast enhancement of mammograms[J]. IEEE Trans Med Imag,1992,11(3):392-406.
[115]Kim J K, Park J M, Song K S, Park H W. Adaptive mammographic image enhancement using first derivative and local statistics[J]. IEEE Trans Med Imaging,1997,16(5): 495-502.
[116]Bovis K and Singh S. Enhancement technique evaluation using quantitative measures on digital mammograms[A]. In Proceeding of the 5th International Workshop on Digital Mammography[C], Toronto, Canada,2000:547-553.
[117]Suckling J, Parker J, Dance D et al. The mammographic images analysis society digital mammogram database[J], Exerpta Medica, International Congress Series,1069: 375-378.
[118]Meijering E. A chronology of interpolation:From ancient astronomy to modern signal and image processing[J]. Proceedings of the IEEE,2002,90(3):319-342.
[119]Lehmann T M, Gonner C and Spitzer K. Survey:Interpolation methods in medical image processing[J]. IEEE Trans on Medical Image,1999,18(11):1049-1075.
[120]Taylor W D, Hsu E, Krishnan K R, MacFall J R. Diffusion Tensor Imaging: Background, Potential, and Utility in Psychiatric Research[J]. Biol. Psychiatry,2004, 55:201-207.
[121]Jiang H, Moloney C. A new direction adaptive scheme for image interpolation[A]. In Processing of International Conference on Image Processing[C].2002:369-372.
[122]韩鸿宾.临床磁共振成像序列设计与应用[M].北京：北京大学医学出版社,2003.
[123]朱礼涛.核磁共振脑图像的分割和形状分析[D].北京：中科院自动化所,2004.
[124]江贵平.磁共振运动伪影消除与扩散张量成像技术研究[D].广州：南方医科大学,2005.
[125]Tuch D. Diffusion MRI of complex tissue structure[D]. Massachusetts Institute of Technology,2002.
[126]Le Bihan D, Breton E. Imagerie de diffusion in vivo par resonance magnetique nuclearire[J]. CR Acad Sci Paris,1985,301:1109-1112.
[127]Basser P J, Mattiello J, LeBihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo[J]. Journal of Magnetic Resonance B,1994,103:247-254.
[128]Le Bihan D. Molecular diffusion nuclear magnetic resonance imaging[J]. Magn Reson Q,1991,7(1):1-30.
[129]林富春.基于扩散张量成像的模式分类、纤维束分析方法及应用[D].北京：中科院自动化所,2006.
[130]Basser P J, Pajevic S, Pierpaoli C, et al. In vivo fiber tractography using DT-MRI data[J]. Magn Reson Med,2000,44(4):625-632.
[131]Parker G J, Haroon H A, Wheeler-Kingshott C A. A framework for a streamline-based probabilistic index of connectivity using a structural interpretation of MRI diffusion measurements [J]. J Magn Reson Imaging,2003,18(2):242-254.
[132]Westin C F, Maier S E, Mamata H, et al. Processing and visualization for diffusion tensor MRI[J]. Med Image Anal,2002,6(2):93-108.
[133]Tschumperle D, Deriche R. Variational frameworks for DT-MRI estimation, regularization and visualization[A]. In Proceedings of ICCV [C],2003:116-121.
[134]Coulon O, Alexander D C, Arridge S. Diffusion tensor magnetic resonance image regularization[J]. Med Image Anal,2004,8(1):47-67.
[135]Tschumperle D, Deriche R. Diffusion tensor regularization with constraints preservation[A]. In Proceedings of CVPR[C],2001:948-953.
[136]Xu D, Mori S, Shen D, et al. Spatial normalization of diffusion tensor fields[J]. Magn Reson Med,2003,50(1):175-182.
[137]Sullivan E V, Pfefferbaum A. Diffusion tensor imaging in normal aging and neuropsychiatric disorders [J]. Eur J Radiol,2003,45(3):244-255.
[138]Bankman I N. Handbook of medical imaging, processing and analysis[M]. New York: Academic,2000:393-420.
[139]Ostuni J L, Santha A K S, Mattay V S, et al. Analysis of interpolation effects in the reslicing of functional MR images[J]. Journal of Computer Assisted Tomography, 1997,21:803-810.
[140]Moller T, Machiraju R, Mueller K and Yagel R. Evaluation and design of filters using a Taylor series expansion[J]. IEEE Trans on Visualization and Computer Graphics, 1997,3:184-199.
[141]Ruttimann U E, Andreason P J and Rio D. Head motion during positron emission tomography:is it significant? [J]. Psychiatry Research:Neuroimaging,1995,61: 43-51.
[142]Rowland S W. Computer implementation of image reconstruction formulas[A]. In Image Reconstruction from Projections:Implementation and Applications[C]. Berlin, Germany:Springer-Verlag,1979.
[143]Grevera G J and Udupa J K. An objective comparison of 3D image interpolation methods[J]. IEEE Trans Med Image,1998,17(4):642-652.
[144]李将云.图像处理中的插值和缩放若干技术研究[D].杭州：浙江大学,2002.
[145]Unser M, Aldroubi A, and Eden M. Fast B-splines transforms for continuous image representation and interpolation[J]. IEEE Trans Pattern Anal Image Processing,1991, 13:277-285.
[146]Unser M, Thevenaz P, and Yaroslavsky L. Convolution-based interpolation for fast, high-quality rotation of images[J]. IEEE Trans Image Processing,1995,4:1371-1381.
[147]Blu T, Thevenaz P, Unser M. Linear interpolation revitalized[J]. IEEE Transactions on Image Processing,2004,13(5):710-719.
[148]Goshtasby A, Truner D A and Ackerman L V. Matching of Tomographic slices for interpolation[J]. IEEE Trans Med Img,1992,11(4):507-516.
[149]Higgins W E, Orlick C J and Ledell B E. Nonlinear filtering approach to 3-D gray-scale image interpolation[J]. IEEE Trans Med Imag,1996,15(4):580-587.
[150]Tam R C and Fournier A. Image Interpolation using unions of spheres[J]. Visual Computer,1998,14:401-414.
[151]Ballester C,Bertalmio M, Caselles V et al. Filling-in by joint interpolation of vector fields and gray levels[J]. IEEE Trans Image Proc,2001,10(8):1200-1211.
[152]Gao Q and Yin F F. Two-Dimensional refinement scheme of direction-based interpolation with discrete orthogonal polynomial decomposition[J]. SPIE,1997,3031: 12-18.
[153]Plaziac N. Image Interpolation Using Neural Networks[J]. IEEE Trans Image Proc, 1999,8(11):1647-1217.
[154]Morse B S, Schwartzwald D. Isophote-based interpolation[A]. In IEEE Processing of International Conference on Image Processing[C].1998:227-231.
[155]Li X. Orchard MT. New edge-directed interpolation[J]. IEEE Transactions on Image Processing,2001,10:1521-1527.
[156]Lu X, Hong P S, Smith M J. An efficient directional image interpolation method[A]. In Processing of IEEE International Conference on Acoustics, Speech, and Signal[C], 2003:97-100.
[157]Carrato S, Tenze L. A high quality 2 X image interpolator[J]. IEEE Signal Processing Letters,2000,7:132-134.
[158]谢美华,王正明.基于图像梯度信息的插值方法[J].中国图像图形学报,2005,10(7)： 856-861.
[159]Thevenaz P, Blu T and Unser M. Interpolation revisited [J]. IEEE Trans on Medical Imaging,2000,19(7):739-758.
[160]Pajevic S, Aldroubi A, Basser P J. A continuous tensor field approximation of discrete DT-MRI data for extracting microstructural and architectural features of tissue[J]. Journal of Magnetic Resonance,2002,154:85-100.
[161]Castano-Moraga C A, Rodriguez-Florido M A, Alvarez L, Westin C F, Ruiz-Alzola, J. Anisotropic Interpolation of DT-MRI[A]. In Proceedings of the 7th International Conference on Medical Image Computing and Computer-Assisted Intervention [C], Saint-Malo, France,2004:343-350.
[162]Thurnhofer S and Mitra S. Edge-enhanced image zooming[J]. Optical Engineering, 1996,35(7):1862-1870.
[163]Schaum A. Theory and design of local interpolators [J]. CVGIP:Graph Models Image Processing,1993,55(6):464-481.
[164]HauBecker H, Jahne B. A tensor approach for local structure analysis in multi-dimensional images [A]. In Proceedings 3D Image Analysis and Synthesis [C], Universitat Erlangen-Nurnberg,1996.
[165]Knutsson H. Representing local structure using tensors [A]. In 6th Scandinavian Conference on Image Analysis[C], Oulu, Finland,1989:244-251.
[166]Knutsson H, and Andersson M. What's so good about quadrature filters? [A]. In 6th 2003 IEEE International Conference on Image Processing[C],2003(111):61-64.
[167]Bigun J and Granlund G H. Optimal orientation detection of linear symmetry[A]. In IEEE First International Conference on Computer Vision[C], London,1987,433-438.
[168]Jahne B. Spatio-Temporal Image Processing, Theory and Applications[M]. LNCS, vol 751, Springer, Berlin,1993.
[169]Jahne B. Digital Image Processing:First-order Tensor Representation[M]. Springer-Verlag Berlin Heidelberg,1997.
[170]Theuβl T, Hauser H, Groller E. Mastering windows:Improving reconstruction[A]. In Proceedings of IEEE Volume Visualization[C],2000:101-108.
[171]Carey W K, Chung D B and Hemami S S. Regularity-preserving image interpolation[J]. IEEE Trans Image Processing,1999,8:1293-1297.
[172]Munoz A, Blu T and Unser M. Least-squares image resizing using finite difference[J]. IEEE Trans Image Processing,2001,10:1365-1378.
[173]王欣,王德隽.离散信号的滤波[M].北京：电子工业出版社,2002,6.
[174]邹谋炎.反卷积和信号复原[M].北京：国防工业出版社,2001.
[175]Aubert G and Kornprobst P. Mathematical problems in image processing-Partial differential equations and the calculus of variations [M].北京：世界图书出版公司北京公司,2005.
[176]Grafakos L. Classical and modern Fourier analysis[M]. Beijing:China Machine Press, 2006.
[177]Joham M and Zoltowski M D. Interpretation of the Multi-Stage Nested Wiener Filter in the Krylov Subspace Framework[R]. Technical Report TUM-LNS-TR-00-6, Munich University of Technology, November,2000.
[178]Lim JS. Two-Dimensional Signal and Image Processing[M], Prentice-Hall,1990.
[179]Lee J S. Digital image enhancement and noise filtering by use of local statistics[J]. IEEE Trans Pattern Analysis and Machine Intelligence,1980,2(2): 165-168.
[180]尚晓清,王军锋,宋国乡.基于Bayesian估计和Wiener滤波的阈值去噪方法[J].光子学报,2003,32(7)：889-891.
[181]赵艳明,全子一.一种有效的小波-Wiener滤波去噪算法[J].北京邮电大学学报,2004,27(4)：41-45.
[182]闫华,刘琚,李道真,孙建德.基于增量维纳滤波和空间自适应规整的超分辨率图像复原[J].电子与信息学报,2005,27(1)：35-38.
[183]侯建华,田金文,柳健.一种迭代小波域维纳滤波算法[J].华中科技大学学报：自然科学版,2006,34(4)：24-26.
[184]侯建华,田金文,柳健.一种小波域与空域相结合的图像滤波方法[J].红外与激光工程,2006,35(1)：122-126.
[185]Das M. An improved adaptive wiener filter for de-noising and signal detection[A]. In Proceedings of the IASTED International Conference Signal and Image Processing[C], Honolulu, USA, August,2005:226-230.
[186]Bigun J and Granlund G H. Optimal orientation detection of linear symmetry[A]. In proceeding 1st IEEE International Conference on Computer Vision[C], London, England, June,1987:433-438.
[187]Bigun J and Granlund G H, and Wiklund J. Multidimensional orientation estimation with applications to texture analysis and optical flow[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1991,13(8):775-790.
[188]Thomas I. Anisotropic adaptation and structure detection[R]. Technical Report F11, Institute for Applied Mathematics, University of Hamburg, Germany, Aug,1999.
[189]Nagel H H and Gehrke A. Spatiotemporally adaptive estimation and segmentation of OF-field[A]. In 5th European Conference on Computer Vision[C], volume 1407 of Lecture Notes in Computer Science, Springer, Freiburg, Germany,1998:86-102.
[190]Middendorf M and Nagel H. H. Empirically convergent adaptive estimation of grayvalue structure tensors[A]. In Pattern Recognition[C], volume 2449 of Lecture Notes in Computer Science, Springer, Berlin,2002:66-74.
[191]Van Den Boomgaard R and van de Weijer J. Robust estimation of orientation for texture analysis[A]. In Proceeding. Second International Workshop on Texture Analysis and Synthesis[C], Copenhagen, June 2002.
[192]Kothe U. Edge and junction detection with an improved structure tensor[A]. In Pattern Recognition[C], volume 2781 of Lecture Notes in Computer Science, Springer, Berlin, 2003:25-32.
[193]Brox T, Weickert J, Burgeth B, and Mrazek P. Nonlinear structure tensor[R]. Technique Report 113, Department of Mathematics, Saarland University, Germany, October,2004.
[194]Weickert J. Anisotropic diffusion in image processing[M]. Teubner, Stuttgart,1998.
[195]Tshumperle D and Deriche R. Orthonormal vector sets regularization with PDE's and applications [J]. International Journal of Computer Vision,2002,50(3):237-252.
[196]Weickert J and Brox T. Diffusion and regularization of vector- and matrix-valued images[A]. In Inverse Problems, Image Analysis, and Medical Imaging[C], volume 313 of Contemporary Mathematics, AMS, Providence,2002:251-268.
[197]Gerig G, Kubler O, Kikinis R and Jolesz F A. Nonlinear anisotropic filtering of MRI data[J]. IEEE Transactions on Medical Imaging,1992,11:221-232.
[198]Charbonnier P, Blanc-Feraud L, Aubert G, and Barlaud M. Deterministic edge-preserving regularization in computed imaging[J]. IEEE transactions on image processing,1997,6(2):298-311.
[199]Dibos F and Koepfler G. Global total variation minimization[J]. SIAM Journal on Numerical Analysis,2000,33(2):646-664.
[200]Andreu F, Ballester C, Caselles B, and Mazon J. Minimizing total variation flow[J]. Differential and Integral Equations,2001,14:321-360.
[201]Andreu F, Ballester C, Caselles B, and Mazon J. Qualitative properties of the total variation flow[J]. Journal of Functional Analysis,2002,188(2):516-547.
[202]Bellettini G, Caselles V, and Novaga M. The total variation flow in RN[J]. Journal of Differential Equations,2002,184(2):475-525.
[203]Catte F, Lions P-L, Morel J-M, Coll T. Image selective smoothing and edge detection by nonlinear diffusion[J]. SIAM Journal of Numerical Analysis,1992,29:182-193.
[204]Weickert J. Recursive separable schemes for nonlinear diffusion filters[A]. Scale-space theory in computer vision[C], LNCS 1252, Springer, Berlin,1997:260-271.
[205]程建刚,田捷,何余良,杨鑫.基于非线性扩散滤波的指纹增强算法[J].自动化学报,2004,30(6)：854-862.