基于MRI的股骨头软骨厚度测量方法的研究
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摘要
作为人体髋关节的重要组成部分,股骨头发生病变甚至坏死的机率很高,目前这仍然是医学领域尚未解决的疑难问题之一。股骨头软骨破坏受损是股骨头发生病变的重要早期表现之一。因此,及时准确地评价关节软骨对于病情的诊断和治疗方法的确定起着非常重要的作用。核磁共振成像(Magnetic Resonance Imaging, MRI)技术以其无电离辐射损害、对软骨组织敏感等优点,成为目前测量和评价关节软骨的最佳无创性方法。利用计算机分析和处理MR图像,实现对软骨厚度的测量和评价,已经逐渐成为医学图形处理领域的一个研究热点。对这项技术的研究将会大大提高诊断的效率和治疗的效果,也符合医学图像处理的发展趋势。本文就是以MR图像中股骨头软骨的图像分割、厚度测量、三维重建等几项关键的检测技术为主要研究内容的。
在图像分割方面,结合了不同分割方法各自的优点,提出了融合多种信息的多步分割算法,实现了对MR图像中股骨头软骨的快速自动分割。针对经过预处理的MR图像,通过Hough变换计算出股骨头的圆心位置,结合股骨头的解剖学尺寸约束,提取出图像中的感兴趣区域,并实现粗分割。利用自适应阈值的Canny边缘检测算子提取目标区域的边缘,按照提出的准则对粗分割区域内的边缘进行噪声消除,检测出精确的股骨头软骨内外边缘,进而提取出内外边缘之间的图像信息,实现对软骨的准确分割。
在厚度测量方面,在MR成像平面内实现对软骨厚度的测量是目前的主要技术手段。通过建立平面薄面体的数学模型和MR摄影过程的仿真信号,在理论上证明了基于高斯二阶微分的零交叉法对于二维MR断层图像中目标特征厚度测量的有效性,仿真实验和MR成像实验进一步验证了这个结论的正确性。在此基础上,针对由软骨表面形状和断层成像引起的软骨厚度的过测量问题,提出了基于形状约束的过测量误差校正算法,对断层图像中得到的测量厚度进行校正,准确地获得了软骨的真实厚度信息,实现了在二维MR断层图像中股骨头软骨的准确测量。
在三维MR图像中直接测量股骨头软骨的空间厚度将是测量技术的发展趋势,因为这种方法无需校正环节,在原理上更符合测量的物理意义。由于MR图像断层间的距离远大于图像中像素间的距离,因此对断层图像层间进行插值是实现空间测量的必要前提。提出了基于灰度区域分割的线性插值与匹配插值混合的插值算法,将线性插值的高效性和匹配插值的精确性有机结合,较好地实现了断层图像间的快速准确插值,使空间体素具有各向同性特征。在此基础上,将基于高斯二阶微分的零交叉法拓展到三维空间,用Hessian矩阵简化了复杂的高斯函数三维卷积运算,实现了对软骨空间厚度的直接测量。实验表明这是一种有效的测量方法。
在三维重建方面,利用经典的三角剖分方法实现了股骨头软骨的表面重建。针对由密集的量化数据点带来的表面粗糙问题,提出了层间错位的数据点遴选方法,成功地消除了重建表面上的梯田效应。针对空间直接剖分产生的耗时问题,提出了改进的Delaunay三角剖分方法,在保证了重建表面质量的前提下,有效地减少了计算时间,提高了重建效率,增强了三维重建方法的应用性。重建的软骨三维模型中包含了软骨的形状特征和厚度信息,对于医学诊断、康复监测以及软骨置换术等治疗方法具有重要的参考价值。
As one of important parts in human hip joint, the femoral head has high probability to get lesion or even to become necrotic. This is still one of the knotty problems in medical field at present. The damage on the femoral cartilage is one of the major features in early stage of diseases on the femoral head. Thus, it is very important to evaluate the articular cartilage timely and accurately in order to make a diagnosis and to determine the therapeutic method. With the advantage of no damage of ionizing radiation, and being sensitive to the cartilage tissue, the technology of Magnetic Resonance Imaging is regarded as the best noninvasive method of cartilage detection and evaluation at present. Using the computer to analyze and process the MR images, and to estimate and evaluate the thickness of cartilage, has been gradually considered as a research hotspot of medical image processing. Research on this technology will greatly improve the efficiency of diagnosis and the effect of treatment, which is also coincident with the development trend of medical image processing. In this thesis, the main research content is several key detection techniques in MR images for femoral cartilage, including image segmentation, thickness measurement, 3D reconstruction.
On the aspects of image segmentation, combining respective advantages of various methods, a multi-step segmentation based on information fusion was proposed to implement fast automatic segmentation of the femoral cartilage in MR images. After pretreatment, the MR images were used to calculate the center of the femoral head by imposing the Hough transform. Combining the anatomic constrain of the femoral head, the region of interest (ROI) was selected and the rough segmentation was realized. The image edges of the object region were then extracted using the adaptive thresholding Canny detector. According to the properties of the pixel on femoral cartilage edge, we labeled these edges and removed the noise edges according to the custom rules to acquire the exact edges of the femoral cartilage. Finally, the femoral cartilage was segmented by extracting the image information between the cartilage edges.
On the aspects of thickness measurement, the main technique at the present time is to measure the thickness of cartilage in the MR image plane. By building mathematical modal of 2D sheet structure and simulating signals in the process of the MR photographing, it was proved theoretically that the zero-crossing method based on the second directional derivatives of Gaussian blurring was effective to measure the sheet structure thickness in 2D MR images. The simulation experiments and MR photographing experiments further verified the conclusion. Based on it, aiming at the problem of overestimation due to the surface shape of cartilage and the slice imaging, a correcting algorithm based on shape constraint was proposed to correct the in-plane thickness, and the real thickness value of femoral cartilage in the 2D MR images was obtained accurately.
Measuring the cartilage thickness of femoral head directly from the 3D MR images will be the trend in the development of measuring technology. Without need of correction, this method is more significance physically in the principle of measurement. The distance between neighbor slices in MR images is much bigger than that between the neighbor pixels in the slice image, so it’s necessary to interpolate between the image slices in order to performance the spatial measurement. A mixed interpolation algorithm based on segmentation of gray region was proposed, which combined the efficiency of linear interpolation and the accuracy of matching interpolation organically, and achieved a good interpolation of the images among the slices to make the voxel isotropic. On the basis of this, the zero-crossing method based on the second directional derivatives of Gaussian blurring was extended to 3D space. The complex 3D convolution of Gaussian function was simplified by Hessian matrix to realize the measuring the spatial thickness of cartilage directly. Experiments showed that this is the affective method of measurement.
On the aspects of 3D reconstruction, the classical triangulation methods were used to reconstruct the surfaces of the femoral head cartilage. Aiming at the problems of surface roughening caused by the dense quantitative points data set, a data points selection method based on cross dislocation was proposed to eliminate the terrace effect on the reconstruct surface successfully. To solve the problem of time consuming during the spatial direct triangulation, an improving Delaunay triangulation method was proposed, which not only guaranteed the quality of the surface reconstruction, reduced the calculating time effectively, but also improved the reconstruction efficiency, strengthened the applicability of the 3D reconstruction method. The reconstructed 3D model of the femoral head cartilage included both the shape features and the thickness information of the cartilage, which could possess important reference value to medical diagnosis, rehabilitation monitoring and many treatment methods, such as cartilage replacement.
在图像分割方面,结合了不同分割方法各自的优点,提出了融合多种信息的多步分割算法,实现了对MR图像中股骨头软骨的快速自动分割。针对经过预处理的MR图像,通过Hough变换计算出股骨头的圆心位置,结合股骨头的解剖学尺寸约束,提取出图像中的感兴趣区域,并实现粗分割。利用自适应阈值的Canny边缘检测算子提取目标区域的边缘,按照提出的准则对粗分割区域内的边缘进行噪声消除,检测出精确的股骨头软骨内外边缘,进而提取出内外边缘之间的图像信息,实现对软骨的准确分割。
在厚度测量方面,在MR成像平面内实现对软骨厚度的测量是目前的主要技术手段。通过建立平面薄面体的数学模型和MR摄影过程的仿真信号,在理论上证明了基于高斯二阶微分的零交叉法对于二维MR断层图像中目标特征厚度测量的有效性,仿真实验和MR成像实验进一步验证了这个结论的正确性。在此基础上,针对由软骨表面形状和断层成像引起的软骨厚度的过测量问题,提出了基于形状约束的过测量误差校正算法,对断层图像中得到的测量厚度进行校正,准确地获得了软骨的真实厚度信息,实现了在二维MR断层图像中股骨头软骨的准确测量。
在三维MR图像中直接测量股骨头软骨的空间厚度将是测量技术的发展趋势,因为这种方法无需校正环节,在原理上更符合测量的物理意义。由于MR图像断层间的距离远大于图像中像素间的距离,因此对断层图像层间进行插值是实现空间测量的必要前提。提出了基于灰度区域分割的线性插值与匹配插值混合的插值算法,将线性插值的高效性和匹配插值的精确性有机结合,较好地实现了断层图像间的快速准确插值,使空间体素具有各向同性特征。在此基础上,将基于高斯二阶微分的零交叉法拓展到三维空间,用Hessian矩阵简化了复杂的高斯函数三维卷积运算,实现了对软骨空间厚度的直接测量。实验表明这是一种有效的测量方法。
在三维重建方面,利用经典的三角剖分方法实现了股骨头软骨的表面重建。针对由密集的量化数据点带来的表面粗糙问题,提出了层间错位的数据点遴选方法,成功地消除了重建表面上的梯田效应。针对空间直接剖分产生的耗时问题,提出了改进的Delaunay三角剖分方法,在保证了重建表面质量的前提下,有效地减少了计算时间,提高了重建效率,增强了三维重建方法的应用性。重建的软骨三维模型中包含了软骨的形状特征和厚度信息,对于医学诊断、康复监测以及软骨置换术等治疗方法具有重要的参考价值。
As one of important parts in human hip joint, the femoral head has high probability to get lesion or even to become necrotic. This is still one of the knotty problems in medical field at present. The damage on the femoral cartilage is one of the major features in early stage of diseases on the femoral head. Thus, it is very important to evaluate the articular cartilage timely and accurately in order to make a diagnosis and to determine the therapeutic method. With the advantage of no damage of ionizing radiation, and being sensitive to the cartilage tissue, the technology of Magnetic Resonance Imaging is regarded as the best noninvasive method of cartilage detection and evaluation at present. Using the computer to analyze and process the MR images, and to estimate and evaluate the thickness of cartilage, has been gradually considered as a research hotspot of medical image processing. Research on this technology will greatly improve the efficiency of diagnosis and the effect of treatment, which is also coincident with the development trend of medical image processing. In this thesis, the main research content is several key detection techniques in MR images for femoral cartilage, including image segmentation, thickness measurement, 3D reconstruction.
On the aspects of image segmentation, combining respective advantages of various methods, a multi-step segmentation based on information fusion was proposed to implement fast automatic segmentation of the femoral cartilage in MR images. After pretreatment, the MR images were used to calculate the center of the femoral head by imposing the Hough transform. Combining the anatomic constrain of the femoral head, the region of interest (ROI) was selected and the rough segmentation was realized. The image edges of the object region were then extracted using the adaptive thresholding Canny detector. According to the properties of the pixel on femoral cartilage edge, we labeled these edges and removed the noise edges according to the custom rules to acquire the exact edges of the femoral cartilage. Finally, the femoral cartilage was segmented by extracting the image information between the cartilage edges.
On the aspects of thickness measurement, the main technique at the present time is to measure the thickness of cartilage in the MR image plane. By building mathematical modal of 2D sheet structure and simulating signals in the process of the MR photographing, it was proved theoretically that the zero-crossing method based on the second directional derivatives of Gaussian blurring was effective to measure the sheet structure thickness in 2D MR images. The simulation experiments and MR photographing experiments further verified the conclusion. Based on it, aiming at the problem of overestimation due to the surface shape of cartilage and the slice imaging, a correcting algorithm based on shape constraint was proposed to correct the in-plane thickness, and the real thickness value of femoral cartilage in the 2D MR images was obtained accurately.
Measuring the cartilage thickness of femoral head directly from the 3D MR images will be the trend in the development of measuring technology. Without need of correction, this method is more significance physically in the principle of measurement. The distance between neighbor slices in MR images is much bigger than that between the neighbor pixels in the slice image, so it’s necessary to interpolate between the image slices in order to performance the spatial measurement. A mixed interpolation algorithm based on segmentation of gray region was proposed, which combined the efficiency of linear interpolation and the accuracy of matching interpolation organically, and achieved a good interpolation of the images among the slices to make the voxel isotropic. On the basis of this, the zero-crossing method based on the second directional derivatives of Gaussian blurring was extended to 3D space. The complex 3D convolution of Gaussian function was simplified by Hessian matrix to realize the measuring the spatial thickness of cartilage directly. Experiments showed that this is the affective method of measurement.
On the aspects of 3D reconstruction, the classical triangulation methods were used to reconstruct the surfaces of the femoral head cartilage. Aiming at the problems of surface roughening caused by the dense quantitative points data set, a data points selection method based on cross dislocation was proposed to eliminate the terrace effect on the reconstruct surface successfully. To solve the problem of time consuming during the spatial direct triangulation, an improving Delaunay triangulation method was proposed, which not only guaranteed the quality of the surface reconstruction, reduced the calculating time effectively, but also improved the reconstruction efficiency, strengthened the applicability of the 3D reconstruction method. The reconstructed 3D model of the femoral head cartilage included both the shape features and the thickness information of the cartilage, which could possess important reference value to medical diagnosis, rehabilitation monitoring and many treatment methods, such as cartilage replacement.
引文
1杨滨,杨柳.关节软骨的MRI研究进展.中国运动医学杂志. 2008, 27(1): 113-116
2 P.R. Kornaat, S.B. Reeder, S. Koo, et al. MR Imaging of Articular Cartilage at
1.5T and 3.0T: Comparison of SPGR and SSFP Sequences. Osteoarthritis and Cartilage. 2005, 13(4): 338-344
3 F. Bloch. Nuclear Induction. Physical Review. 1946, 70: 460-474
4 E.M. Purcell, H.C. Torrey and R.V. Pound. Resonance Absorption by Nuclear Magnetic Moments in a Solid. Physical Review. 1946, 69: 37-38
5 P.C. Lauterbor. Image Formation by Induced Local Interaction: Examples Employing Nuclear Magnetic Resonance. Nature. 1973, 242: 190-191
6程克斌,屈辉.关节软骨的MRI研究现状.中华放射学杂志. 2004, 38(1): 97-100
7吴振华,张军.应用MRI技术提高关节软骨疾病的诊断水平.中华放射学杂志. 2003, 37(11): 965-966
8 D.G. Disler, T.R. McGauley, C.G. Kelman, et al. Fat-suppressed Three-dimensional Spoiled Gradient Echo MR Imaging of Hyaline Cartilage Defects in the Knee: Comparison with Standard MR imaging and arthroscopy. American J. of Roentgenology. 1996, 167(1): 127-132
9罗小平,华兰娇,丁爱民,等.中低磁场3D-FS-SPGR结合三维重建技术对关节软骨损伤的诊断价值.中国临床医学影像杂志. 2005, 16(3): 153-155
10 P.I. Kuikka, M.J.Kiuru, M.H. Niva, et al. Sensitivity of Routine 1.0-Tesla Magnetic Resonance Imaging Versus Arthroscopy as Gold Standard in Fresh Traumatic Chondral Lesions of the Knee in Young Adults. Arthroscopy. 2006, 22(10): 1033-1039
11 Y.Y. Ho, A.J. Stanley, J.H. Hui, et al. Postoperative Evaluation of the Knee After Autologous Chondrocyte Implantation: What Radiologists Need to Know. Radio Graphics. 2007, 27(1): 207-220
12 K. Miura, Y. Ishibashi, E. Tsuda, et al. Results of Arthroscopy Fixation of Osteochondritis Dissecans Lesion of the Knee with Cylindrical AutogenousOsteochondral Plugs. The American J. of Sports Medicine. 2007, 35(2): 216-222
13 S. Trattnig, S.A. Millington, P. Szomolanyi, et al. MR Imaging of Osteochondral Grafts and Autologous Chondrocyte Implantation. European Radiology. 2007, 17(1): 103-118
14林瑶,田捷.医学图像分割方法综述.模式识别与人工智能. 2002, 15(2): 192-204
15陈强,周则明,屈颖歌,等.左心室核磁共振图像的自动分割.计算机学报. 2005, 28(6): 991-999
16罗彤,陈裕泉.视觉注意引导和区域竞争控制的医学图像分割.浙江大学学报(工学版). 2007, 41(11): 1797-1800
17 F.M. Hall and G. Wyshak. Thickness of Articular Cartilage in the Normal Knee. J. of Bone and Joint Surgery. American Volume. 1980, 62(3): 408-413
18 M.D.V. Leersum, M.E. Schweitzer, F. Gannon, et al. Thickness of Patello Femoral Articular Cartilage as Measured on MR Imaging: Sequence Comparison of Accuracy, Reproducibility, and Interobserver Variation. Skeletal Radiology. 1995, 24(6):431-435
19 N.Y. Afoke, P.D. Byers and W.C. Hutton. Contact Pressures in the Human Hip Joint. J. of Bone and Joint Surgery. British Volume. 1987, 69(4); 536-541
20 C.A. McGibbon, W.E. Palmer and D.E. Krebs. A General Computing Method for Spatial Cartilage Thickness from Co-planar MRI. Medical Engineering and Physics.1998, 20: 169–176
21 K. Nakanishi, H. Tanaka, N. Sugano, et al. MR-based Three-dimensional Presentation of Cartilage Thickness in the Femoral Head. Eurpean Radiology. 2001, 11(11): 2178-2183
22 T.L. Boegard, O. Rudling, I.F. Petersson, et al. Distribution of MR-detected Cartilage Defects of the Patellofemoral Joint in Chronic Knee Pain. Osteoarthritis and Cartilage. 2003, 11: 494-498
23 C.A. McGibbon and C.A. Trahan. Measurement Accuracy of Focal Cartilage Defects from MRI and Correlation of MRI Graded Lesions with Histology: A Preliminary Study. Osteoarthritis and Cartilage. 2003, 11: 483-493
24 J.C. Gamio, J.S. Bauer, K.Y. Lee, et al. Combined Image ProcessingTechniques for Characterization of MRI Cartilage of the Knee. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. Shanghai, 2005, 3043-3046
25 F. Eckstein, F. Cicuttini, J.P. Raynauld, et al. Magnetic Resonance Imaging of Articular Cartilage in Knee Osteoarthritis: Morphological Assessment. Osteoarthritis and Cartilage. 2006, 14: A46-A75
26 S. Solloway, C.J. Taylor, C.E. Hutchinson, et al. Quantification of Articular Cartilage from MR Images Using Active Shape Models. Lecture Notes in Computer Science. 1996, 1065: 400-411
27 M. Takao, N. Sugano, T. Nishii, et al. Application of Three-dimensional Magnetic Resonance Image Registration for Monitoring Hip Joint Diseases. Magnetic Resonance Imaging. 2005, 23: 665-670
28 J.H. Naish, E. Xanthopoulos, C.E. Hutchinson, et al. MR Measurement of Articular Cartilage Thickness Distribution in the Hip. Osteoarthritis and Cartilage. 2006, 14: 967-973
29 F. Eckstein, G. Ateshian, R. Burgkart, et al. Proposal for a Nomenclature for Magnetic Resonance Imaging Based Measures of Articular Cartilage in Osteoarthritis. Osteoarthritis and Cartilage. 2006, 14: 974-983
30 C.A. McGibbon. Inter-rater and Intra-rater Reliability of Subchondral Bone and Cartilage Thickness Measurement from MRI. Magnetic Resonance Imaging. 2003, 21: 707-714
31谢英杰,叶志前.医学图像可视化与相关技术及应用.国外医学生物医学工程分册. 2001, 24(3): 114-117
32江贵平,张煜,陈武凡,等.基于MRI数据的人体器官三维重建.第一军医大学学报. 2005, 25(1): 15-17
33 K. Horsch, M.L. Giger, L.A. Venta, et al. Automatic Segmentation of Breast Lesions on Ultrasound. Med. Phys. 2001, 28(8): 1652-1659
34 K. Horsch, M.L. Giger, L.A. Venta, et al. Computerized Diagnosis of Breast Lesions on Ultrasound. Med. Phys. 2002, 29(2): 157-164
35 K. Horsch, M.L. Giger, C.E. Metz. Prevalence Scaling: Applications to an Intelligent Workstation for the Diagnosis of Breast Cancer. Academic Radiology. 2008, 15(11): 1446-1457
36 L.P. Clarke, R.P. Velthuizen, S. Phuphanich, et al. MRI: Stability of Three Supervised Segmentation Techniques. Magnetic Resonance Imaging. 1993, 11(1): 95-106
37 L.P. Clarke, R.P. Velthuizen, M.A. Camacho, et al. MRI Segmentation: Methods and Applications. Magnetic Resonance Imaging. 1995, 13(3):343-368
38 K. Drukker, M.L. Giger, C.J. Vyborny, et al. Computerized Detection and Classification of Cancer on Breast Ultrasound. Academic Radiology. 2004, 11(5): 526-535
39 K.G.A. Gilhuijs, K. Drukker, A. Touw, et al. Interactive Three Dimensional Inspection of Patient Setup in Radiation Therapy Using Digital Portal Images and Computed Tomography Data. International J. of Radiation Oncology Biology Physics. 1996, 34(4): 873-885
40 E.A. Ashton, M.J. Berg, K.J. Parker, et al. Segmentation and Feature Extraction Techniques, with Applications to MRI Head Studies. Magnetic Resonance in Medicine. 1995, 33(5): 670-677
41 E.A. Ashton, K.J. Parker, M.J. Berg, et al. A Novel Volumetric Feature Extraction Technique with Applications to MR images. IEEE Trans on Medical Imaging. 1997, 16(4): 365-371
42 D. Boukerroui, A. Baskurt, J.A. Noble, et al. Segmentation of Ultrasound Images-Multiresolution 2D and 3D Algorithm Based on Global and Local Statistics. Pattern Recognition Letters. 2003, 24(4-5): 779-790
43 D. Boukerroui, J.A. Noble, M.C. Robini, et al. Enhancement of Contrast Regions in Suboptimal Ultrasound Images with Application to Echocardiography. Ultrasound in Medicine and Biology. 2001, 27(12): 1583-1594
44 J.A. Noble and D. Boukerroui. Ultrasound Image Segmentation: A Survey. IEEE Trans on Medical Imaging. 2006, 25(8): 987-1010
45 E.J. McWalter, W. Wirth, M. Siebert, et al. Use of Novel Interactive Input Devices for Segmentation of Articular Cartilage from Magnetic Resonance Images. Osteoarthritis and Cartilage. 2005, 13(1): 48-53
46 E.J. McWalter, D.C. Wilson, D.F. Kacher, et al. Three-dimensional Patellar Kinematics in Weightbearing Flexion Using Open MRI. J. of Biomechanics.2006, 39(1): S72
47 M.H. Brem, P.K. Lang, G. Neumann, et al. Magnetic Resonance Image Segmentation Using Semi-automated Software for Quantification of Knee Articular Cartilage-Initial Evaluation of a Technique for Paired Scans. Skeletal Radiol. 2009, 38: 505-511
48 J. Duryea, G. Neumann, M.H. Brem, et al. Novel Fast Semi-automated Software to Segment Cartilage for Knee MR acquisitions. Osteoarthritis and Cartilage. 2007, 15(5): 487-492
49 R.A. Zoroofi, Y. Sato, M. Khanmohammadi, et al. Fully Automatic Localization of the Articular Space in MR Images of the Hip Joint. International J. of Computer Assisted Radiology and Surgery. 2006, 1(1): 464-465
50 A. Baniasadipour, R.A. Zoroofi, Y. Sato, et al. A Fully Automated Method for Segmentation and Thickness Map Estimation of Femoral and Acetabular Cartilages in 3D CT Images of the Hip. ISPA2007, Istanbul, Turkey, 2007, 92-97
51曾妍婷,朱宏擎.基于改进遗传算法的图像分割识别方法.武汉理工大学学报. 2004, 28(3): 435-443
52徐旦华,鲍旭东,舒华忠.基于区域划分和改进C-V法的医学图像分割方法.东南大学学报(自然科学版). 2006, 36(5): 863-868
53王毅,牛奕龙,田沄.基于改进遗传算法的最佳熵多阈值三维医学图像分割算法.西北工业大学学报. 2007, 25(3): 442-445
54刘复昌,尤建洁,郭亮,等.结合Hough变换与测地线轮廓模型的MR图像坐心室自动分割.计算机辅助设计与图形学学报. 2007, 19(10): 1292-1297
55 P.D. Rushfeldt, R.W. Mann and W.H. Harris. Improved Techniques for Measuring in Vitro the Geometry and Pressure Distribution in the Human Acetabulum-I. Ultrasonic Measurement of Acetabular Surfaces, Sphericity and Cartilage Thickness. J. of Biomechanics. 1981, 14(4): 253-255
56 V.E. Modest, M.C. Murphy and R.W. Mann. Optical Verification of a Technique for in situ Ultrasonic Measurement of Articular Cartilage Thickness. J. of Biomechanics. 1989, 22(2): 171-173
57 J.C. Waterton, V. Rajanayagam, B.D. Ross, et al. Magnetic ResonanceMethods for Disease Progression in Rheumatoid Arthritis. Magnetic Resonance Imaging. 1993, 11(7): 1033-1038
58 M.D. Robson, R.J. Hodgson, N.J. Herrod, et al. A combined analysis and Magnetic Resonance Imaging Technique for Computerized Automatic Measurement of Cartilage Thickness in the Distal Interphalangeal Joint. Magnetic Resonance Imaging. 1995, 13(5): 709-718
59 S. Trattnig. Overuse of Hyaline Cartilage and Imaging. European J. of Radiology. 1997, 25(3): 188-198
60 J.S. Wayne, C.W. Brodrick and N. Mukherjee. Measurement of Articular Cartilage Thickness in the Articulated Knee. Annals of Biomedical Engineering. 1998, 26(1): 96-102
61 J.S. Jurvelin, T. Rasanen, P. Kolmonens, et al. Comparison of Optical, Needle Probe and Ultrasonic Techniques for the Measurement of Articular Cartilage Thickness. J. of Biomechanics. 1995, 28(2): 231-235
62 S.L. Myers, K. Dines, D.A. Brandt, et al. Experimental Assessment by High Frequency Ultrasound of Articular Cartilage Thickness and Osteoarthritic Changes. J. of Rheumatology. 1995, 22(1): 109-116
63 Z.A. Cohen, D.M. McCarthy, S.D. Kwak, et al. Knee Cartilage Topography, Thickness, and Contact Areas from MRI: In-vivo Calibration and In-vivo Measurements. Osteoarthritis and Cartilage. 1999, 7(1): 95-109
64 B. Kladny, H. Bail, B. Swoboda, et al. Cartilage Thickness Measurement in Magnetic Resonance Imaging. Osteoarthritis and Cartilage. 1996, 4(3): 181-186
65 Y. Song, J.M. Greve, D.R. Carter, et al. Articular Cartilage MR Imaging and Thickness Mapping of a Loaded Knee Joint Before and After Meniscectomy. Osteoarthritis and Cartilage. 2006, 14(8): 728-737
66 S. Koo, G.E. Gold and T.P. Andriacchi. Considerations in Measuring Cartilage Thickness Using MRI: Factors Influencing Reproducibility and Accuracy. Osteoarthritis and Cartilage. 2005, 13(9): 782-789
67 Y. Sato, K. Nakanishi, H. Tanaka, et al. A Fully Automated Method for Segmentation and Thickness Determination of Hip Joint Cartilage from 3D MR Data. International Congress Series. 2001, 1230: 352-358
68 H. Graichen, J. Jakob, R.E. Rothe, et al. Validation of Cartilage Volume and Thickness Measurements in the Human Shoulder with Quantitative Magnetic Resonance Imaging. Osteoarthritis and Cartilage. 2003, 11(7): 475-482
69 L.L. Greaves, M.K. Gilbart, A. Yung, et al. Deformation and Recovery of Cartilage in the Intact Hip Under Physiological Loads Using 7T MRI. J. of Biomechanics. 2009, 42(3): 349-354
70 J.S. Duncan and N. Ayache. Medical Image Analysis: Progress over Two Decades and the Challenges Ahead. IEEE Trans. on Pattern Analysis and Machine Intelligence. 2000, 22(1): 85-106
71 J. Wei, B. Sahiner, L.M. Hadjiiski, et al. Computer-aided Detection of Breast Masses on Full Field Digital Mammograms. Medical Physics. 2005, 32(9): 2827-2838
72 A. Wyler, V. Bousson, C. Bergot, et al. Comparison of MR-arthrography and CT-arthrography in Hyaline Cartilage Thickness Measurement in Radiographically Normal Cadaver Hips with Anatomy as Gold Standard. Osteoarthritis and Cartilage. 2009, 17(1): 19-25
73 K. Sugimoto, Y. Takakura, Y. Tohno, et al. Cartilage Thickness of the Talar Dome. The J. of Arthroscopic and Related Surgery. 2005, 21(4): 401-404
74 J.C. Gamio, J.S. Bauer, R. Stahl, et al. Inter-subject Comparison of MRI Knee Cartilage Thickness. Medical Image Analysis. 2008, 12(2): 120-135
75 I. Mechlenburg, J.R. Nyengaard, J. Gelineck, et al. Cartilage Thickness in the Hip Joint Measured by MRI and Stereology-a Methodological Study. Osteoarthritis and Cartilage. 2007, 15(4): 366-371
76 P.A. Hardy, P. Nammalwar and S. Kuo. Measuring the Thickness of Articular Cartilage from MR Images. J. of Magnetic Resonance Imaging. 2001, 13: 120-126
77 P.A. Hardy, C.K. Poh, Z. Liao, et al. The Use of Magnetic Resonance Imaging to Measure the Local Ultrafiltration Rate in Hemodialyzers. J. of Membrane Science. 2002, 204(1-2): 195-205
78 S. Prevrhal, J.C. Fox, J.A. Shepherd, et al. Accuracy of CT-based Thickness Measurement of Thin Structures: Modeling of Limited Spatial Resolution in All Three Dimensions. Medical Physics. 2003, 30(1): 1-8
79 G. Dougherty and D. Newman. Measurement of Thickness and Density of Thin Structures by Computed Tomography: A Simulation Study. Med Phys.1999, 26 (7):1341–1348
80 R.M. Hoogeveen, C.J.G. Bakker and M.A. Viergever. Limits to the Accuracy of Vessel Diameter Measurement in MR Angiography. J. of Magnetic Resonance Imaging. 1998, 8(6):1228-1235
81 N. Sugano, T. Kubo, K. Takaoka, et al. Diagnostic Criteria for Non-traumatic Osteonecrosis of the Femoral Head. A Multicentre Study. J. of Bone and Joint Surgery Br. 2004, 81(4):590-595
82 Y.J. Kim, D. Jaramillo, M.B. Millis, et al. Assessment of Early Osteoarthritis in Hip Dysplasia with Delayed Gadolinium-Enhanced Magnetic Resonance Imaging of Cartilage. J. of Bone and Joint Surgery Am 2003, 85(10):1987-1992
83 K.Y. Lee, J.N. Masi, C.A. Sell, et al. Computer-aided Quantification of Focal Cartilage Lesions Using MRI: Accuracy and Initial Arthroscopic Comparison. Osteoarthritis and Cartilage. 2005, 13(8): 728-737
84 S. Akhtar, C.L. Poh and R.I. Kitney. An MRI Derived Articular Cartilage Visualizaiton Framework. Osteoarthritis and Cartilage. 2007, 15(9): 1070-1085
85 C.A. McGibbon, J. Bencardino, E.D. Yeh, et al. Accuracy of Cartilage and Subchondral Bone Spatial Thickness Distribution From MRI. J. of Magnetic Resonance Imaging. 2003, 17(6): 703-715
86 Y. Cheng, Y. Sato, S. WANG, et al. Accuracy Analysis for Thickness Determination of Thin Sheet Structure Under the Influence of Proximate Thin Sheet Structure Using MR Images. J. of the Chinese Institute of Engineers. 2007, 30: 661-673
87 Y. Cheng, S. Wang, T. Yamazaki, et al. Hip Cartilage Thickness Measurement Accuracy Improvement. Computerized Medical Imaging and Graphics. 2007, 31(8): 643-655
88 J.F. Greenleaf, T.S. Tu and E.H. Wood. Computer Generated 3D Oscilloscopic Images and Associated Techniques for Display and Study of the Spatial Distribution of Pulmonary Blood Flow. IEEE Trans on Nuclear Science. 1970, 17(3): 353-359
89 S.D. Roth. Ray Casting for Modelign Solids. Computer Graphics and Image Processing. 1982, 18: 109-144
90 W. Lee. Footprint Evaluation for Volume Rendering. Proceedings of the 17th Annual Conference on Computer Graphics and Interactive Techniques, Dallas, Texas, USA, 1990: 367-376
91 A.H. Bytyqi, G. Bachmann, M. Rieke, et al. Cell-by-cell Reconstruction in Reaggregates from Neonatal Gerbil Retina Begins from the Inner Retina and is Promoted by Retinal Pigmented Epithelium. European J. of Neuroscience. 2007, 26(6): 1560-1574
92 C. Lu and J.G. Dunham. Shape Matching Using Polygon Approximation and Dynamic Alignment. Pattern Recognition Letters. 1993, 14(12): 945-949
93 A. Gelder and K. Kim. Direct Volume Rendering with Shading via Three-dimensional Textures. Proceedings on Volume Visualization, San Francisco, CA, USA, 1998: 23-30
94 L. Che, G.T. Hermen, R.A. Reynolds, et al. Surface Shading in the Cuberille Environment. Computer Graphics and Applications. 1985, 5(12): 33-43
95 W.E. Lorensen and H.E. Cline. Marching Cube: A High Resolution 3D Surface Construction Algorithm. Computer Graphics. 1987: 21(4): 163-169
96 S. Chan and E.O. Purisima. Molecular Surface Generation Using Marching Tetrahedra. J. of Computational Chemistry. 1998, 19(11): 1268-1277
97 H.E. Cline, W.E. Lorensen, S. Ludke, et al. Two Algorithms for the Three-dimensional Reconstruction of Tomograms. Medical Physics. 1988, 15(3): 320-327
98 C. Sohler. Fast Reconstruction of Delaunay Triangulations. Computational Geometry-Theory and Applications. 2005, 31(3): 166-178
99 J. Itoh and R. Sinclair. Thaw: A Tool for Appoximating Cut Loci on a Triangulation of a Surface. Experimental Mathematics. 2004, 13(3): 309-325
100 T. Bonfort, P. Sturm and P. Cargallo. General Specular Surface Triangulation. Lecture Notes in Computer Science. 2006, 3852: 872-881
101 C. Svensson, H. Aanaes and F. Kahl. Structure Estimation and Surface Triangulation of Deformable Objects. Lecture Notes in Computer Science. 2003, 2749: 709-716
102 H. Lin, C. Tai and G. Wang. A Mesh Reconstruction Algorithm Driven by an Intrinsic Property of a Point Cloud. Computer Aided Design. 2004: 36(1): 1-9
103 R. Chaine. A Geometric Convection Approach of 3D Reconstruction. Proceedings of the Eurographics/ACM Siggraph Symposuim on Geometry Processing, Aachen, Germany, 2003: 218-229
104 J. Huang and G.H. Menq. Combinational Manifold Mesh Reconstruction and Optimization from Unorganized Points with Arbitrary Meshes. Computer Aided Design. 2002, 34(2): 149-165
105 A. Bowyer. Computing Dirichlet Tessellations. The Computer Journal. 1981, 24(2): 162-166
106 D. Waston. Computing the N-dimensional Delaunay Tessellation with Application to Voronoi Poltopes. The Computer Journal. 1981, 24(2): 167-172
107 C. Lawson. Generation a Triangular Grid with Application of Contour Plotting Technical Memo. Jet Propulsion Laboratory, Pasadena, California, 1972: 229
108史松伟,任秉银.三维稀疏散乱点集的直接三角剖分新方法.哈尔滨工业大学学报. 2005, 37(10): 1318-1320
109 D.L. Parker, Y.P. Du and W.L. Davis. The Voxel Sensitivity Function in Fourier Transform Imaging: Applications to Magnetic Resonance Angiography. Magnetic Resonance in Medicine. 1995, 33(2): 156-162
110 J. Lllingworth and J. Kittler. A Survey of the Hough Transform. Computer Vision, Graphics and Image Processing. 1988, 44(1): 87-116
111王云祥,吕衡发,张书琴.人体解剖学.吉林科学技术出版社. 2000: 20-25
112 J. Canny. A computational Approach to Edge Detection. IEEE Trans on Pattern Analysis and Machine Intelligence. 1986, 8(6): 679-698
113李牧.机器人无标定视觉伺服关键技术的研究.哈尔滨工业大学博士论文. 2008: 29-34
114 N. Otsu. A Threshold Selection Method from Gray-level Histograms. IEEE Trans on System, Man and Cybernetics. 1979, 9(1): 62-66
115 M. Vaidyanathan, L.P. Clarke, L.O. Hall, et al. Monitoring Brain Tumor Response to Therapy Using MRI Segmentation. Magnetic Resonance Imaging. 1997, 15(3): 323-334
116 D.L. Collins, A.P. Zijdenbos, V. Kollokian, et al. Design and Construction of aRealistic Digital Brain Phantom. IEEE Trans. on Medical Imaging. 1998, 17(3): 463-468
117 M.C. Steckner, D.J. Dros, and F.S. Prato. Computing the Modulation Transfer Function of a Magnetic Resonance Imager. Medical Physices. 1994, 21(3): 483-489
118 A. Rengle, M. Armenean, R. Bolbos, et al. A Dedicated Two-element Phased Array Receiver Coil for High Resolusion MRI of Rat Knee Cartilage at 7T. Proceedings of the 29th Annual International Conference of the IEEE EMBS. Lyon, 2007: 3886-3889
119 J.P. Pelletier, J.P. Raynauld, F. Abram, et al. A New Non-invasive Method to Assess Synovitis Severity in Relation to Symptoms and Cartilage Volume Loss in Knee Osteoarthritis Patients Using MRI. Osteoarthritis and Cartilage. 2008, 16(3): S8-S13
120 A.J. Gougoutas, A.J. Wheaton, A. Borthakur, et al. Cartilage Volume Quantification via Live Wire Segmentation. Academic Radiology. 2004, 11(12): 1389- 1395
121 S. Weckbach, T. Mendlik, W. Horger, et al. Quantitative Assessment of Patellar Cartilage Volume and Thickness at 3.0T Comparing a 3D-fast Low Angle Shot Versus a 3D-ture Fast Imaging with Steady-state Precession Sequence for Reproducibility. Investigative Radiology. 2006, 41(2): 189-197
122 M.B. Djuric and Z.R. Djurisic. Frequency Measurement of Distorted Signals Using Fourier and Zero Crossing Techniques. Electric Power System Research. 2008, 78(8): 1407-1415
123 Y. Fujii. Impact Response Measurement of an Accelerometer. Mechanical System and Signal Processing. 2007, 21(5): 2072-2079
124 W.E. Kwok, S.M. Totterman and J. Zhong. 3D Interleaved Water and Fat Image Acquisition with Chemical-shift Correction. Magnetic Resonance in Medicine. 2000. 44(2): 322–330
125张勇,欧宗瑛,秦绪佳,等.基于物质分类的三维空间断层图像匹配插值.计算机辅助设计与图形学学报. 2002, 14(7): 659-663
126 M.B. Djuric and Z.R. Djurisic. Frequency Measurement of Distorted Signals Using Fourier and Zero Crossing Techniques. Electric Power System Research.2008, 78(8): 1407-1415
127 M. Marji and P. Siy. Polygonal Representation of Digital Planar Curves through Dominant Point Detection-a Nonparametric Algorithm. Pattern Recognition. 2004, 37(11): 2113-2130
128 G. Cong and B. Parvin. Robust and Efficient Surface Reconstruction from Contours. The Visual Computer. 2001, 17(4): 199-208
129 M.J. Herbert and C.B. Jones. Contour Correspondence for Serial Section Reconstruction: Complex Scenarios in Palaeontology. Computer and Geosciences. 2001, 27(4): 427-440
130 C. Sohler. Fast Reconstruction of Delaunay Triangulations. Computational Geometry-Theory and Applications. 2005, 31(3): 166-178
131董辰世,汪国昭.一个利用法矢的散乱点集三角剖分算法.计算机学报, 2005, 28(6): 1000-1005
132 M. Gopi, S. Krishnan and C.T. Silva. Surface Reconstruction Based on Lower Dimensional Localized Delaunay Triangulation. Computer Graphics Forum. 2000, 19(3): 467-468
133张永春,达飞鹏,宋文忠.三维散乱点集的曲面三角剖分.中国图像图形学报. 2003, 8A(12): 1379-1387
134 B.K. Choi, H.Y. Shin, Y.I. Yoon, et al. Triangulation of Scattered Data in 3D Space. CAD. 1988, 20(5): 239-248
135 D.H. Douglas and T.K. Peucker. Algorithm for the Reduction of the Number of Points Required to Represent a Line or its Caricature. The Canadian Cartographer. 1973, 10(2): 112-122
2 P.R. Kornaat, S.B. Reeder, S. Koo, et al. MR Imaging of Articular Cartilage at
1.5T and 3.0T: Comparison of SPGR and SSFP Sequences. Osteoarthritis and Cartilage. 2005, 13(4): 338-344
3 F. Bloch. Nuclear Induction. Physical Review. 1946, 70: 460-474
4 E.M. Purcell, H.C. Torrey and R.V. Pound. Resonance Absorption by Nuclear Magnetic Moments in a Solid. Physical Review. 1946, 69: 37-38
5 P.C. Lauterbor. Image Formation by Induced Local Interaction: Examples Employing Nuclear Magnetic Resonance. Nature. 1973, 242: 190-191
6程克斌,屈辉.关节软骨的MRI研究现状.中华放射学杂志. 2004, 38(1): 97-100
7吴振华,张军.应用MRI技术提高关节软骨疾病的诊断水平.中华放射学杂志. 2003, 37(11): 965-966
8 D.G. Disler, T.R. McGauley, C.G. Kelman, et al. Fat-suppressed Three-dimensional Spoiled Gradient Echo MR Imaging of Hyaline Cartilage Defects in the Knee: Comparison with Standard MR imaging and arthroscopy. American J. of Roentgenology. 1996, 167(1): 127-132
9罗小平,华兰娇,丁爱民,等.中低磁场3D-FS-SPGR结合三维重建技术对关节软骨损伤的诊断价值.中国临床医学影像杂志. 2005, 16(3): 153-155
10 P.I. Kuikka, M.J.Kiuru, M.H. Niva, et al. Sensitivity of Routine 1.0-Tesla Magnetic Resonance Imaging Versus Arthroscopy as Gold Standard in Fresh Traumatic Chondral Lesions of the Knee in Young Adults. Arthroscopy. 2006, 22(10): 1033-1039
11 Y.Y. Ho, A.J. Stanley, J.H. Hui, et al. Postoperative Evaluation of the Knee After Autologous Chondrocyte Implantation: What Radiologists Need to Know. Radio Graphics. 2007, 27(1): 207-220
12 K. Miura, Y. Ishibashi, E. Tsuda, et al. Results of Arthroscopy Fixation of Osteochondritis Dissecans Lesion of the Knee with Cylindrical AutogenousOsteochondral Plugs. The American J. of Sports Medicine. 2007, 35(2): 216-222
13 S. Trattnig, S.A. Millington, P. Szomolanyi, et al. MR Imaging of Osteochondral Grafts and Autologous Chondrocyte Implantation. European Radiology. 2007, 17(1): 103-118
14林瑶,田捷.医学图像分割方法综述.模式识别与人工智能. 2002, 15(2): 192-204
15陈强,周则明,屈颖歌,等.左心室核磁共振图像的自动分割.计算机学报. 2005, 28(6): 991-999
16罗彤,陈裕泉.视觉注意引导和区域竞争控制的医学图像分割.浙江大学学报(工学版). 2007, 41(11): 1797-1800
17 F.M. Hall and G. Wyshak. Thickness of Articular Cartilage in the Normal Knee. J. of Bone and Joint Surgery. American Volume. 1980, 62(3): 408-413
18 M.D.V. Leersum, M.E. Schweitzer, F. Gannon, et al. Thickness of Patello Femoral Articular Cartilage as Measured on MR Imaging: Sequence Comparison of Accuracy, Reproducibility, and Interobserver Variation. Skeletal Radiology. 1995, 24(6):431-435
19 N.Y. Afoke, P.D. Byers and W.C. Hutton. Contact Pressures in the Human Hip Joint. J. of Bone and Joint Surgery. British Volume. 1987, 69(4); 536-541
20 C.A. McGibbon, W.E. Palmer and D.E. Krebs. A General Computing Method for Spatial Cartilage Thickness from Co-planar MRI. Medical Engineering and Physics.1998, 20: 169–176
21 K. Nakanishi, H. Tanaka, N. Sugano, et al. MR-based Three-dimensional Presentation of Cartilage Thickness in the Femoral Head. Eurpean Radiology. 2001, 11(11): 2178-2183
22 T.L. Boegard, O. Rudling, I.F. Petersson, et al. Distribution of MR-detected Cartilage Defects of the Patellofemoral Joint in Chronic Knee Pain. Osteoarthritis and Cartilage. 2003, 11: 494-498
23 C.A. McGibbon and C.A. Trahan. Measurement Accuracy of Focal Cartilage Defects from MRI and Correlation of MRI Graded Lesions with Histology: A Preliminary Study. Osteoarthritis and Cartilage. 2003, 11: 483-493
24 J.C. Gamio, J.S. Bauer, K.Y. Lee, et al. Combined Image ProcessingTechniques for Characterization of MRI Cartilage of the Knee. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. Shanghai, 2005, 3043-3046
25 F. Eckstein, F. Cicuttini, J.P. Raynauld, et al. Magnetic Resonance Imaging of Articular Cartilage in Knee Osteoarthritis: Morphological Assessment. Osteoarthritis and Cartilage. 2006, 14: A46-A75
26 S. Solloway, C.J. Taylor, C.E. Hutchinson, et al. Quantification of Articular Cartilage from MR Images Using Active Shape Models. Lecture Notes in Computer Science. 1996, 1065: 400-411
27 M. Takao, N. Sugano, T. Nishii, et al. Application of Three-dimensional Magnetic Resonance Image Registration for Monitoring Hip Joint Diseases. Magnetic Resonance Imaging. 2005, 23: 665-670
28 J.H. Naish, E. Xanthopoulos, C.E. Hutchinson, et al. MR Measurement of Articular Cartilage Thickness Distribution in the Hip. Osteoarthritis and Cartilage. 2006, 14: 967-973
29 F. Eckstein, G. Ateshian, R. Burgkart, et al. Proposal for a Nomenclature for Magnetic Resonance Imaging Based Measures of Articular Cartilage in Osteoarthritis. Osteoarthritis and Cartilage. 2006, 14: 974-983
30 C.A. McGibbon. Inter-rater and Intra-rater Reliability of Subchondral Bone and Cartilage Thickness Measurement from MRI. Magnetic Resonance Imaging. 2003, 21: 707-714
31谢英杰,叶志前.医学图像可视化与相关技术及应用.国外医学生物医学工程分册. 2001, 24(3): 114-117
32江贵平,张煜,陈武凡,等.基于MRI数据的人体器官三维重建.第一军医大学学报. 2005, 25(1): 15-17
33 K. Horsch, M.L. Giger, L.A. Venta, et al. Automatic Segmentation of Breast Lesions on Ultrasound. Med. Phys. 2001, 28(8): 1652-1659
34 K. Horsch, M.L. Giger, L.A. Venta, et al. Computerized Diagnosis of Breast Lesions on Ultrasound. Med. Phys. 2002, 29(2): 157-164
35 K. Horsch, M.L. Giger, C.E. Metz. Prevalence Scaling: Applications to an Intelligent Workstation for the Diagnosis of Breast Cancer. Academic Radiology. 2008, 15(11): 1446-1457
36 L.P. Clarke, R.P. Velthuizen, S. Phuphanich, et al. MRI: Stability of Three Supervised Segmentation Techniques. Magnetic Resonance Imaging. 1993, 11(1): 95-106
37 L.P. Clarke, R.P. Velthuizen, M.A. Camacho, et al. MRI Segmentation: Methods and Applications. Magnetic Resonance Imaging. 1995, 13(3):343-368
38 K. Drukker, M.L. Giger, C.J. Vyborny, et al. Computerized Detection and Classification of Cancer on Breast Ultrasound. Academic Radiology. 2004, 11(5): 526-535
39 K.G.A. Gilhuijs, K. Drukker, A. Touw, et al. Interactive Three Dimensional Inspection of Patient Setup in Radiation Therapy Using Digital Portal Images and Computed Tomography Data. International J. of Radiation Oncology Biology Physics. 1996, 34(4): 873-885
40 E.A. Ashton, M.J. Berg, K.J. Parker, et al. Segmentation and Feature Extraction Techniques, with Applications to MRI Head Studies. Magnetic Resonance in Medicine. 1995, 33(5): 670-677
41 E.A. Ashton, K.J. Parker, M.J. Berg, et al. A Novel Volumetric Feature Extraction Technique with Applications to MR images. IEEE Trans on Medical Imaging. 1997, 16(4): 365-371
42 D. Boukerroui, A. Baskurt, J.A. Noble, et al. Segmentation of Ultrasound Images-Multiresolution 2D and 3D Algorithm Based on Global and Local Statistics. Pattern Recognition Letters. 2003, 24(4-5): 779-790
43 D. Boukerroui, J.A. Noble, M.C. Robini, et al. Enhancement of Contrast Regions in Suboptimal Ultrasound Images with Application to Echocardiography. Ultrasound in Medicine and Biology. 2001, 27(12): 1583-1594
44 J.A. Noble and D. Boukerroui. Ultrasound Image Segmentation: A Survey. IEEE Trans on Medical Imaging. 2006, 25(8): 987-1010
45 E.J. McWalter, W. Wirth, M. Siebert, et al. Use of Novel Interactive Input Devices for Segmentation of Articular Cartilage from Magnetic Resonance Images. Osteoarthritis and Cartilage. 2005, 13(1): 48-53
46 E.J. McWalter, D.C. Wilson, D.F. Kacher, et al. Three-dimensional Patellar Kinematics in Weightbearing Flexion Using Open MRI. J. of Biomechanics.2006, 39(1): S72
47 M.H. Brem, P.K. Lang, G. Neumann, et al. Magnetic Resonance Image Segmentation Using Semi-automated Software for Quantification of Knee Articular Cartilage-Initial Evaluation of a Technique for Paired Scans. Skeletal Radiol. 2009, 38: 505-511
48 J. Duryea, G. Neumann, M.H. Brem, et al. Novel Fast Semi-automated Software to Segment Cartilage for Knee MR acquisitions. Osteoarthritis and Cartilage. 2007, 15(5): 487-492
49 R.A. Zoroofi, Y. Sato, M. Khanmohammadi, et al. Fully Automatic Localization of the Articular Space in MR Images of the Hip Joint. International J. of Computer Assisted Radiology and Surgery. 2006, 1(1): 464-465
50 A. Baniasadipour, R.A. Zoroofi, Y. Sato, et al. A Fully Automated Method for Segmentation and Thickness Map Estimation of Femoral and Acetabular Cartilages in 3D CT Images of the Hip. ISPA2007, Istanbul, Turkey, 2007, 92-97
51曾妍婷,朱宏擎.基于改进遗传算法的图像分割识别方法.武汉理工大学学报. 2004, 28(3): 435-443
52徐旦华,鲍旭东,舒华忠.基于区域划分和改进C-V法的医学图像分割方法.东南大学学报(自然科学版). 2006, 36(5): 863-868
53王毅,牛奕龙,田沄.基于改进遗传算法的最佳熵多阈值三维医学图像分割算法.西北工业大学学报. 2007, 25(3): 442-445
54刘复昌,尤建洁,郭亮,等.结合Hough变换与测地线轮廓模型的MR图像坐心室自动分割.计算机辅助设计与图形学学报. 2007, 19(10): 1292-1297
55 P.D. Rushfeldt, R.W. Mann and W.H. Harris. Improved Techniques for Measuring in Vitro the Geometry and Pressure Distribution in the Human Acetabulum-I. Ultrasonic Measurement of Acetabular Surfaces, Sphericity and Cartilage Thickness. J. of Biomechanics. 1981, 14(4): 253-255
56 V.E. Modest, M.C. Murphy and R.W. Mann. Optical Verification of a Technique for in situ Ultrasonic Measurement of Articular Cartilage Thickness. J. of Biomechanics. 1989, 22(2): 171-173
57 J.C. Waterton, V. Rajanayagam, B.D. Ross, et al. Magnetic ResonanceMethods for Disease Progression in Rheumatoid Arthritis. Magnetic Resonance Imaging. 1993, 11(7): 1033-1038
58 M.D. Robson, R.J. Hodgson, N.J. Herrod, et al. A combined analysis and Magnetic Resonance Imaging Technique for Computerized Automatic Measurement of Cartilage Thickness in the Distal Interphalangeal Joint. Magnetic Resonance Imaging. 1995, 13(5): 709-718
59 S. Trattnig. Overuse of Hyaline Cartilage and Imaging. European J. of Radiology. 1997, 25(3): 188-198
60 J.S. Wayne, C.W. Brodrick and N. Mukherjee. Measurement of Articular Cartilage Thickness in the Articulated Knee. Annals of Biomedical Engineering. 1998, 26(1): 96-102
61 J.S. Jurvelin, T. Rasanen, P. Kolmonens, et al. Comparison of Optical, Needle Probe and Ultrasonic Techniques for the Measurement of Articular Cartilage Thickness. J. of Biomechanics. 1995, 28(2): 231-235
62 S.L. Myers, K. Dines, D.A. Brandt, et al. Experimental Assessment by High Frequency Ultrasound of Articular Cartilage Thickness and Osteoarthritic Changes. J. of Rheumatology. 1995, 22(1): 109-116
63 Z.A. Cohen, D.M. McCarthy, S.D. Kwak, et al. Knee Cartilage Topography, Thickness, and Contact Areas from MRI: In-vivo Calibration and In-vivo Measurements. Osteoarthritis and Cartilage. 1999, 7(1): 95-109
64 B. Kladny, H. Bail, B. Swoboda, et al. Cartilage Thickness Measurement in Magnetic Resonance Imaging. Osteoarthritis and Cartilage. 1996, 4(3): 181-186
65 Y. Song, J.M. Greve, D.R. Carter, et al. Articular Cartilage MR Imaging and Thickness Mapping of a Loaded Knee Joint Before and After Meniscectomy. Osteoarthritis and Cartilage. 2006, 14(8): 728-737
66 S. Koo, G.E. Gold and T.P. Andriacchi. Considerations in Measuring Cartilage Thickness Using MRI: Factors Influencing Reproducibility and Accuracy. Osteoarthritis and Cartilage. 2005, 13(9): 782-789
67 Y. Sato, K. Nakanishi, H. Tanaka, et al. A Fully Automated Method for Segmentation and Thickness Determination of Hip Joint Cartilage from 3D MR Data. International Congress Series. 2001, 1230: 352-358
68 H. Graichen, J. Jakob, R.E. Rothe, et al. Validation of Cartilage Volume and Thickness Measurements in the Human Shoulder with Quantitative Magnetic Resonance Imaging. Osteoarthritis and Cartilage. 2003, 11(7): 475-482
69 L.L. Greaves, M.K. Gilbart, A. Yung, et al. Deformation and Recovery of Cartilage in the Intact Hip Under Physiological Loads Using 7T MRI. J. of Biomechanics. 2009, 42(3): 349-354
70 J.S. Duncan and N. Ayache. Medical Image Analysis: Progress over Two Decades and the Challenges Ahead. IEEE Trans. on Pattern Analysis and Machine Intelligence. 2000, 22(1): 85-106
71 J. Wei, B. Sahiner, L.M. Hadjiiski, et al. Computer-aided Detection of Breast Masses on Full Field Digital Mammograms. Medical Physics. 2005, 32(9): 2827-2838
72 A. Wyler, V. Bousson, C. Bergot, et al. Comparison of MR-arthrography and CT-arthrography in Hyaline Cartilage Thickness Measurement in Radiographically Normal Cadaver Hips with Anatomy as Gold Standard. Osteoarthritis and Cartilage. 2009, 17(1): 19-25
73 K. Sugimoto, Y. Takakura, Y. Tohno, et al. Cartilage Thickness of the Talar Dome. The J. of Arthroscopic and Related Surgery. 2005, 21(4): 401-404
74 J.C. Gamio, J.S. Bauer, R. Stahl, et al. Inter-subject Comparison of MRI Knee Cartilage Thickness. Medical Image Analysis. 2008, 12(2): 120-135
75 I. Mechlenburg, J.R. Nyengaard, J. Gelineck, et al. Cartilage Thickness in the Hip Joint Measured by MRI and Stereology-a Methodological Study. Osteoarthritis and Cartilage. 2007, 15(4): 366-371
76 P.A. Hardy, P. Nammalwar and S. Kuo. Measuring the Thickness of Articular Cartilage from MR Images. J. of Magnetic Resonance Imaging. 2001, 13: 120-126
77 P.A. Hardy, C.K. Poh, Z. Liao, et al. The Use of Magnetic Resonance Imaging to Measure the Local Ultrafiltration Rate in Hemodialyzers. J. of Membrane Science. 2002, 204(1-2): 195-205
78 S. Prevrhal, J.C. Fox, J.A. Shepherd, et al. Accuracy of CT-based Thickness Measurement of Thin Structures: Modeling of Limited Spatial Resolution in All Three Dimensions. Medical Physics. 2003, 30(1): 1-8
79 G. Dougherty and D. Newman. Measurement of Thickness and Density of Thin Structures by Computed Tomography: A Simulation Study. Med Phys.1999, 26 (7):1341–1348
80 R.M. Hoogeveen, C.J.G. Bakker and M.A. Viergever. Limits to the Accuracy of Vessel Diameter Measurement in MR Angiography. J. of Magnetic Resonance Imaging. 1998, 8(6):1228-1235
81 N. Sugano, T. Kubo, K. Takaoka, et al. Diagnostic Criteria for Non-traumatic Osteonecrosis of the Femoral Head. A Multicentre Study. J. of Bone and Joint Surgery Br. 2004, 81(4):590-595
82 Y.J. Kim, D. Jaramillo, M.B. Millis, et al. Assessment of Early Osteoarthritis in Hip Dysplasia with Delayed Gadolinium-Enhanced Magnetic Resonance Imaging of Cartilage. J. of Bone and Joint Surgery Am 2003, 85(10):1987-1992
83 K.Y. Lee, J.N. Masi, C.A. Sell, et al. Computer-aided Quantification of Focal Cartilage Lesions Using MRI: Accuracy and Initial Arthroscopic Comparison. Osteoarthritis and Cartilage. 2005, 13(8): 728-737
84 S. Akhtar, C.L. Poh and R.I. Kitney. An MRI Derived Articular Cartilage Visualizaiton Framework. Osteoarthritis and Cartilage. 2007, 15(9): 1070-1085
85 C.A. McGibbon, J. Bencardino, E.D. Yeh, et al. Accuracy of Cartilage and Subchondral Bone Spatial Thickness Distribution From MRI. J. of Magnetic Resonance Imaging. 2003, 17(6): 703-715
86 Y. Cheng, Y. Sato, S. WANG, et al. Accuracy Analysis for Thickness Determination of Thin Sheet Structure Under the Influence of Proximate Thin Sheet Structure Using MR Images. J. of the Chinese Institute of Engineers. 2007, 30: 661-673
87 Y. Cheng, S. Wang, T. Yamazaki, et al. Hip Cartilage Thickness Measurement Accuracy Improvement. Computerized Medical Imaging and Graphics. 2007, 31(8): 643-655
88 J.F. Greenleaf, T.S. Tu and E.H. Wood. Computer Generated 3D Oscilloscopic Images and Associated Techniques for Display and Study of the Spatial Distribution of Pulmonary Blood Flow. IEEE Trans on Nuclear Science. 1970, 17(3): 353-359
89 S.D. Roth. Ray Casting for Modelign Solids. Computer Graphics and Image Processing. 1982, 18: 109-144
90 W. Lee. Footprint Evaluation for Volume Rendering. Proceedings of the 17th Annual Conference on Computer Graphics and Interactive Techniques, Dallas, Texas, USA, 1990: 367-376
91 A.H. Bytyqi, G. Bachmann, M. Rieke, et al. Cell-by-cell Reconstruction in Reaggregates from Neonatal Gerbil Retina Begins from the Inner Retina and is Promoted by Retinal Pigmented Epithelium. European J. of Neuroscience. 2007, 26(6): 1560-1574
92 C. Lu and J.G. Dunham. Shape Matching Using Polygon Approximation and Dynamic Alignment. Pattern Recognition Letters. 1993, 14(12): 945-949
93 A. Gelder and K. Kim. Direct Volume Rendering with Shading via Three-dimensional Textures. Proceedings on Volume Visualization, San Francisco, CA, USA, 1998: 23-30
94 L. Che, G.T. Hermen, R.A. Reynolds, et al. Surface Shading in the Cuberille Environment. Computer Graphics and Applications. 1985, 5(12): 33-43
95 W.E. Lorensen and H.E. Cline. Marching Cube: A High Resolution 3D Surface Construction Algorithm. Computer Graphics. 1987: 21(4): 163-169
96 S. Chan and E.O. Purisima. Molecular Surface Generation Using Marching Tetrahedra. J. of Computational Chemistry. 1998, 19(11): 1268-1277
97 H.E. Cline, W.E. Lorensen, S. Ludke, et al. Two Algorithms for the Three-dimensional Reconstruction of Tomograms. Medical Physics. 1988, 15(3): 320-327
98 C. Sohler. Fast Reconstruction of Delaunay Triangulations. Computational Geometry-Theory and Applications. 2005, 31(3): 166-178
99 J. Itoh and R. Sinclair. Thaw: A Tool for Appoximating Cut Loci on a Triangulation of a Surface. Experimental Mathematics. 2004, 13(3): 309-325
100 T. Bonfort, P. Sturm and P. Cargallo. General Specular Surface Triangulation. Lecture Notes in Computer Science. 2006, 3852: 872-881
101 C. Svensson, H. Aanaes and F. Kahl. Structure Estimation and Surface Triangulation of Deformable Objects. Lecture Notes in Computer Science. 2003, 2749: 709-716
102 H. Lin, C. Tai and G. Wang. A Mesh Reconstruction Algorithm Driven by an Intrinsic Property of a Point Cloud. Computer Aided Design. 2004: 36(1): 1-9
103 R. Chaine. A Geometric Convection Approach of 3D Reconstruction. Proceedings of the Eurographics/ACM Siggraph Symposuim on Geometry Processing, Aachen, Germany, 2003: 218-229
104 J. Huang and G.H. Menq. Combinational Manifold Mesh Reconstruction and Optimization from Unorganized Points with Arbitrary Meshes. Computer Aided Design. 2002, 34(2): 149-165
105 A. Bowyer. Computing Dirichlet Tessellations. The Computer Journal. 1981, 24(2): 162-166
106 D. Waston. Computing the N-dimensional Delaunay Tessellation with Application to Voronoi Poltopes. The Computer Journal. 1981, 24(2): 167-172
107 C. Lawson. Generation a Triangular Grid with Application of Contour Plotting Technical Memo. Jet Propulsion Laboratory, Pasadena, California, 1972: 229
108史松伟,任秉银.三维稀疏散乱点集的直接三角剖分新方法.哈尔滨工业大学学报. 2005, 37(10): 1318-1320
109 D.L. Parker, Y.P. Du and W.L. Davis. The Voxel Sensitivity Function in Fourier Transform Imaging: Applications to Magnetic Resonance Angiography. Magnetic Resonance in Medicine. 1995, 33(2): 156-162
110 J. Lllingworth and J. Kittler. A Survey of the Hough Transform. Computer Vision, Graphics and Image Processing. 1988, 44(1): 87-116
111王云祥,吕衡发,张书琴.人体解剖学.吉林科学技术出版社. 2000: 20-25
112 J. Canny. A computational Approach to Edge Detection. IEEE Trans on Pattern Analysis and Machine Intelligence. 1986, 8(6): 679-698
113李牧.机器人无标定视觉伺服关键技术的研究.哈尔滨工业大学博士论文. 2008: 29-34
114 N. Otsu. A Threshold Selection Method from Gray-level Histograms. IEEE Trans on System, Man and Cybernetics. 1979, 9(1): 62-66
115 M. Vaidyanathan, L.P. Clarke, L.O. Hall, et al. Monitoring Brain Tumor Response to Therapy Using MRI Segmentation. Magnetic Resonance Imaging. 1997, 15(3): 323-334
116 D.L. Collins, A.P. Zijdenbos, V. Kollokian, et al. Design and Construction of aRealistic Digital Brain Phantom. IEEE Trans. on Medical Imaging. 1998, 17(3): 463-468
117 M.C. Steckner, D.J. Dros, and F.S. Prato. Computing the Modulation Transfer Function of a Magnetic Resonance Imager. Medical Physices. 1994, 21(3): 483-489
118 A. Rengle, M. Armenean, R. Bolbos, et al. A Dedicated Two-element Phased Array Receiver Coil for High Resolusion MRI of Rat Knee Cartilage at 7T. Proceedings of the 29th Annual International Conference of the IEEE EMBS. Lyon, 2007: 3886-3889
119 J.P. Pelletier, J.P. Raynauld, F. Abram, et al. A New Non-invasive Method to Assess Synovitis Severity in Relation to Symptoms and Cartilage Volume Loss in Knee Osteoarthritis Patients Using MRI. Osteoarthritis and Cartilage. 2008, 16(3): S8-S13
120 A.J. Gougoutas, A.J. Wheaton, A. Borthakur, et al. Cartilage Volume Quantification via Live Wire Segmentation. Academic Radiology. 2004, 11(12): 1389- 1395
121 S. Weckbach, T. Mendlik, W. Horger, et al. Quantitative Assessment of Patellar Cartilage Volume and Thickness at 3.0T Comparing a 3D-fast Low Angle Shot Versus a 3D-ture Fast Imaging with Steady-state Precession Sequence for Reproducibility. Investigative Radiology. 2006, 41(2): 189-197
122 M.B. Djuric and Z.R. Djurisic. Frequency Measurement of Distorted Signals Using Fourier and Zero Crossing Techniques. Electric Power System Research. 2008, 78(8): 1407-1415
123 Y. Fujii. Impact Response Measurement of an Accelerometer. Mechanical System and Signal Processing. 2007, 21(5): 2072-2079
124 W.E. Kwok, S.M. Totterman and J. Zhong. 3D Interleaved Water and Fat Image Acquisition with Chemical-shift Correction. Magnetic Resonance in Medicine. 2000. 44(2): 322–330
125张勇,欧宗瑛,秦绪佳,等.基于物质分类的三维空间断层图像匹配插值.计算机辅助设计与图形学学报. 2002, 14(7): 659-663
126 M.B. Djuric and Z.R. Djurisic. Frequency Measurement of Distorted Signals Using Fourier and Zero Crossing Techniques. Electric Power System Research.2008, 78(8): 1407-1415
127 M. Marji and P. Siy. Polygonal Representation of Digital Planar Curves through Dominant Point Detection-a Nonparametric Algorithm. Pattern Recognition. 2004, 37(11): 2113-2130
128 G. Cong and B. Parvin. Robust and Efficient Surface Reconstruction from Contours. The Visual Computer. 2001, 17(4): 199-208
129 M.J. Herbert and C.B. Jones. Contour Correspondence for Serial Section Reconstruction: Complex Scenarios in Palaeontology. Computer and Geosciences. 2001, 27(4): 427-440
130 C. Sohler. Fast Reconstruction of Delaunay Triangulations. Computational Geometry-Theory and Applications. 2005, 31(3): 166-178
131董辰世,汪国昭.一个利用法矢的散乱点集三角剖分算法.计算机学报, 2005, 28(6): 1000-1005
132 M. Gopi, S. Krishnan and C.T. Silva. Surface Reconstruction Based on Lower Dimensional Localized Delaunay Triangulation. Computer Graphics Forum. 2000, 19(3): 467-468
133张永春,达飞鹏,宋文忠.三维散乱点集的曲面三角剖分.中国图像图形学报. 2003, 8A(12): 1379-1387
134 B.K. Choi, H.Y. Shin, Y.I. Yoon, et al. Triangulation of Scattered Data in 3D Space. CAD. 1988, 20(5): 239-248
135 D.H. Douglas and T.K. Peucker. Algorithm for the Reduction of the Number of Points Required to Represent a Line or its Caricature. The Canadian Cartographer. 1973, 10(2): 112-122