Optimal occlusion of teeth using planar structure information

In orthodontics, occlusion is defined as the relationship between the upper and lower sets of teeth when the jaws are brought together. Understanding the nature of occlusion has important significance for the diagnosis and treatment of occlusal dysfunction and for planning reconstructive dentistry. The materials of study are 31 pairs of manually aligned dental study models. The upper and lower models are independently digitized using a laser surface scanner. Occlusion can be recovered by detecting and aligning a set of planes on the models. We describe a two-step procedure for determining the occlusal relationship using digitized dental models. The first step is a coarse alignment using four planar structures that are detected by K-means clustering, followed by principal component analysis. The second step is a refinement process using a variant of the iterative closest point technique. The quantitative results show that the algorithm is accurate, with an average measurement discrepancy of 0.74 mm between the physical and virtual models.

[1]  P Millstein,et al.  An evaluation of occlusal contact marking indicators. A descriptive quantitative method. , 2001, Journal of the American Dental Association.

[2]  Mathieu Desbrun,et al.  Variational shape approximation , 2004, SIGGRAPH 2004.

[3]  Sim Heng Ong,et al.  Optimal Occlusion Of Teeth , 2006, 2006 9th International Conference on Control, Automation, Robotics and Vision.

[4]  Michael Garland,et al.  Hierarchical face clustering on polygonal surfaces , 2001, I3D '01.

[5]  James S Hodges,et al.  Comparing maximum intercuspal contacts of virtual dental patients and mounted dental casts. , 2002, The Journal of prosthetic dentistry.

[6]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  R B Winstanley,et al.  An appraisal of the literature on centric relation. Part III. , 2000, Journal of oral rehabilitation.

[8]  Hao Zhang,et al.  Mesh Segmentation via Spectral Embedding and Contour Analysis , 2007, Comput. Graph. Forum.

[9]  Zhengyou Zhang,et al.  Iterative point matching for registration of free-form curves and surfaces , 1994, International Journal of Computer Vision.

[10]  Robert B. Fisher,et al.  Estimating 3-D rigid body transformations: a comparison of four major algorithms , 1997, Machine Vision and Applications.

[11]  R B Winstanley,et al.  An appraisal of the literature on centric relation. Part II. , 2000, Journal of oral rehabilitation.

[12]  Naokazu Yokoya,et al.  A Robust Method for Registration and Segmentation of Multiple Range Images , 1995, Comput. Vis. Image Underst..

[13]  D. Ritchie,et al.  Protein docking using spherical polar Fourier correlations , 2000, Proteins.

[14]  R B Winstanley,et al.  An appraisal of the literature on centric relation. Part I. , 2008, Journal of oral rehabilitation.

[15]  Patrick J. Flynn,et al.  A Survey Of Free-Form Object Representation and Recognition Techniques , 2001, Comput. Vis. Image Underst..

[16]  D Fischer,et al.  Molecular surface representations by sparse critical points , 1994, Proteins.

[17]  Berthold K. P. Horn,et al.  Closed-form solution of absolute orientation using unit quaternions , 1987 .

[18]  R. Nussinov,et al.  Molecular recognition via face center representation of a molecular surface. , 1996, Journal of Molecular Graphics.

[19]  H. Wolfson,et al.  Small molecule recognition: solid angles surface representation and molecular shape complementarity. , 1999, Combinatorial chemistry & high throughput screening.

[20]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..

[21]  Ayellet Tal,et al.  Hierarchical mesh decomposition using fuzzy clustering and cuts , 2003, ACM Trans. Graph..

[22]  Andrew W. Fitzgibbon,et al.  An Experimental Comparison of Range Image Segmentation Algorithms , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Ross T. Whitaker,et al.  Partitioning 3D Surface Meshes Using Watershed Segmentation , 1999, IEEE Trans. Vis. Comput. Graph..

[24]  P L Millstein A method to determine occlusal contact and noncontact areas: preliminary report. , 1984, The Journal of prosthetic dentistry.

[25]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[26]  Toyohiko Hayashi,et al.  Computer-aided determination of occlusal contact points for dental 3-D CAD , 2006, Medical and Biological Engineering and Computing.

[27]  D J Rinchuse,et al.  A three-dimensional comparison of condylar change between centric relation and centric occlusion using the mandibular position indicator. , 1995, American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics.

[28]  Patrick J. Flynn,et al.  Pair-Wise Range Image Registration: A Study in Outlier Classification , 2002, Comput. Vis. Image Underst..

[29]  Gérard G. Medioni,et al.  Object modeling by registration of multiple range images , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[30]  Yehezkel Lamdan,et al.  Geometric Hashing: A General And Efficient Model-based Recognition Scheme , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[31]  Zoltán R. Bárdosi,et al.  Dentition planning with image-based occlusion analysis , 2006, International Journal of Computer Assisted Radiology and Surgery.

[32]  H. Wolfson,et al.  Examination of shape complementarity in docking of Unbound proteins , 1999, Proteins.

[33]  H. Wolfson,et al.  Shape complementarity at protein–protein interfaces , 1994, Biopolymers.