全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

应用于人体关节缺损修复的建模与可视化

DOI: 10.11834/jig.20140216

Keywords: 关节|骨骼分割|建模|定量分析

Full-Text   Cite this paper   Add to My Lib

Abstract:

目的关节缺损疾病治疗目前存在的主要问题是缺乏精确的关节模型以及个体化的修复方案,为此提出量化关节骨缺损的精确建模与可视化方法。方法利用骨骼图像增强、多模态影像融合、关节结构分割、关节病变结构建模与定量分析等核心关键技术,从关节CT或MRI影像中构建和恢复缺损关节的空间立体结构,为关节缺损信息提供量化参数和3维模型,从而帮助医生快速准确地对关节缺损疾病进行诊疗。结果针对建模与可视化方法中的核心关键技术进行了深入研究,实验结果显示上述方法能够为关节缺损修复提供精确的3维量化模型。结论基于CT、MRI影像的关节结构建模与可视化技术为评价骨缺损大小提供了精确有效的方法,在关节盂或肱骨结构等疾病的诊疗方面具有重要的临床意义,此项技术的发展对关节缺损疾病的修复发挥着重要作用。

References

[1]  Aslan M S, Ali A, Rara H, et al. A novel 3D segmentation of vertebral bones from volumetric ct images using graph cuts[C]//Proceedings of Advances in Visual Computing. Berlin Heidelberg: Springer, 2009: 519-528.
[2]  Chu W, Ma L, Song J, et al. An iterative image registration algorithm by optimizing similarity measurement[J]. Journal of Research of the National Institute of Standards and Technology, 2010, 115(1): 1-6.
[3]  Zhang Q, Wang L, Li H, et al. Similarity-based multimodality image fusion with shiftable complex directional pyramid[J]. Pa-ttern Recognition Letters, 2011, 32(13): 1544-1553.
[4]  更多...
[5]  Yang Y, Park D S, Huang S, et al. Medical image fusion via an effective wavelet-based approach[J]. EURASIP Journal on Advances in Signal Processing, 2010: 44.
[6]  Calder J, Tahmasebi A M, Mansouri A R. A variational appr
[7]  Dipaola M J, Jazrawi L M, Rokito A S, et al. Management of humeral and glenoid bone loss associated with glenohumeral instability: results with anatomical bone grafting[J]. Bulletin of the NYU Hospital for Joint Diseases, 2010, 68(4): 245.
[8]  Song W W. Study on key technologies of modeling for rehabilitation of femoral head. Dalian[D]: Dalian University of Technology, 2008. 宋卫卫. 股骨头修复建模关键技术研究[D]. 大连:大连理工大学, 2008.
[9]  Choplin R H, Henley C N, Edds E M, et al. Total hip arthroplasty in patients with bone deficiency of the acetabulum[J]. Radiographics, 2008, 28(3): 71-86.
[10]  Park M J, Garcia M M S G. Arthroscopic remplissage with bankart repair for the treatment of glenohumeral instability with hill-sachs lesion[J]. University of Pennsylvania Orthopedic Journal, 2010, 20:36-41.
[11]  Ljungquist K L, Butler R B, Griesser M J, et al. Prediction of coracoid thickness using a glenoid width-based model: implications for bone reconstruction procedures in chronic anterior shoulder instability[J]. Journal of Shoulder and Elbow Surgery, 2012, 21(6): 815-821.
[12]  Skendzel J G, Sekiya J K. Diagnosis and management of humeral head bone loss in shoulder instability[J]. The American Journal of Sports Medicine, 2012, 40(11): 2633-2644.
[13]  Bois A J, Fening S D, Polster J, et al. Quantifying glenoid bone loss in anterior shoulder instability reliability and accuracy of 2-Dimensional and 3-Dimensional computed tomography measurement techniques[J]. The American Journal of Sports Medicine, 2012, 40(11): 2569-2577.
[14]  Rerko M A, Pan X, Donaldson C, et al. Comparison of various imaging techniques to quantify glenoid bone loss in shoulder instability[J]. Journal of Shoulder and Elbow Surgery, 2012, 22(4): 528-534.
[15]  Zhang J, Yan C H, Chui C K, et al. Fast segmentation of bone in CT images using 3D adaptive thresholding[J]. Computers in Biology and Medicine, 2010, 40(2): 231-236.
[16]  Huang C Y, Luo L J, Lee P Y, et al. Efficient segmentation algorithm for 3D bone models construction on medical images[J]. Journal of Medical and Biological Engineering, 2011, 31(6): 375-386.
[17]  Lee S, Park S H, Shim H, et al. Optimization of local shape and appearance probabilities for segmentation of knee cartilage in 3-D MR images[J]. Computer Vision and Image Understanding, 2011, 115(12): 1710-1720.
[18]  Davies R H. Learning Shape: Optimal Models for Analyzing Natural Variability[M]. Manchester: University of Manchester, 2002.
[19]  Li C M, Kao C Y, Gore J C, et al. Minimization of region-scalable fitting energy for image segmentation[J]. IEEE Transactions on Image Processing, 2008, 17 (10):1940-1949.
[20]  Hantes M E, Venouziou A, Bargiotas K A, et al. Repair of an anteroinferior glenoid defect by the latarjet procedure: quantitative assessment of the repair by computed tomography[J]. Arthroscopy: The Journal of Arthroscopic & Related Surgery, 2010, 26(8): 1021-1026.
[21]  Diederichs G, Seim H, Meyer H, et al. CT-based patient-specific modeling of glenoid rim defects: a feasibility study[J]. American Journal of Roentgenology, 2008, 191(5): 1406-1411.
[22]  Pluim J P W, Maintz J B A, Viergever M A. Mutual-information-based registration of medical images: a survey[J]. IEEE Transactions on Medical Imaging, 2003, 22(8): 986-1004.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133