Semi-automated segmentation and visualisation of outer bone cortex from medical images

被引:24
作者
Division of Biomechanics and Engineering Design, Katholieke Universiteit Leuven, B-3001 Heverlee, Belgium [1 ]
不详 [2 ]
机构
[1] Division of Biomechanics and Engineering Design, Katholieke Universiteit Leuven
[2] Division of Production, Machine Design and Automation, Katholieke Universiteit Leuven
来源
Comput. Methods Biomech. Biomed. Eng. | 2006年 / 1卷 / 65-77期
关键词
Automated filter procedure; Medical image based segmentation; Multiple bone type; Outer bone cortex;
D O I
10.1080/10255840600604474
中图分类号
学科分类号
摘要
Good segmentation of the outer bone cortex from medical images is a prerequisite for applications in the field of finite element analysis, surgical planning environments and personalised, case dependent, bone reconstruction. However, current segmentation procedures are often unsatisfactory. This study presents an automated filter procedure to generate a set of adapted contours from which a surface mesh can be deduced directly. The degree of interaction is user determined. The bone contours are extracted from the patients CT data by quick grey value segmentation. An extended filter procedure then only retains contour information representing the outer cortex as more specific internal loops and shape irregularities are removed, tailoring the image for the above-mentioned applications. The developed medical image based design methodology to convert contour sets of multiple bone types, from tibia tumour to neurocranium, is reported and discussed. © 2006 Taylor & Francis Ltd.
引用
收藏
页码:65 / 77
页数:12
相关论文
共 17 条
[1]  
Barber C.B., Dobkin D.P., Huhdanpaa H.T., The quickhull algorithm for convex hulls, ACM Trans. Math. Software, 22, 4, pp. 469-483, (1996)
[2]  
Cootes T.F., Taylor C.J., Cooper D.H., Graham J., Active shape models-their training and application, Comput. Vision Image Und, 61, 1, pp. 38-59, (1995)
[3]  
Davies R.H., Learning shape: Optimal models for analysing natural variability, (2002)
[4]  
Davies R.H., Twining C.J., Cootes T.F., Waterton J., Taylor C.J., A minimum description length approach to statistical shape modelling, IEEE Trans. Med. Imaging, 21, pp. 525-537, (2002)
[5]  
Hieu L.C., Bohez E., vander Sloten J., Vatcharaporn E., Binh P.H., An P.V., Oris P., Design for medical rapid prototyping of cranioplasty implant, Rapid Prototyping J, 9, 3, pp. 175-186, (2003)
[6]  
Jans G., vander Sloten J., Gobin R., van der Perre G., van Audekercke R., Mommaerts M., Computer-aided craniofacial surgical planning implemented in CAD software, Comput. Aided Surg, 4, pp. 117-128, (1999)
[7]  
Kass M., Witkin A., Terzopoulos D., Snakes: Active contour models, Int. J. Comput. Vision, 1, 4, pp. 321-331, (1987)
[8]  
Kaus M.R., Lorenz C., Truyen R., Lobregt S., Weese J., Automated 3D PDM construction from segmented images using deformable model, IEEE Trans. Med. Imaging, 22, 8, pp. 1005-1013, (2003)
[9]  
Kozak J., Nesper M., Fischer M., Goggelmann A., Hassfeld S., Wetter T., Semiautomated registration using new markers for assessing the accuracy of a navigation system, Comput. Aided Eng, 7, pp. 11-24, (2002)
[10]  
Lorensen W.E., Cline H.E., Marching cubes: A high resolution 3D surface construction algorithm, Comput. Graph, 21, 4, pp. 163-169, (1987)