3D biplanar reconstruction of scoliotic vertebrae using statistical models

This paper presents a new 3D reconstruction method of the scoliotic vertebrae of a spine, using two conventional radiographic views (postero-anterior and lateral), and global prior knowledge on the geometrical structure of each vertebra. This geometrical knowledge is efficiently captured by a statistical deformable template integrating a set of admissible deformations, expressed by the first modes of variation in the Karhunen-Loeve expansion of the pathological deformations observed on a representative scoliotic vertebra population. The proposed reconstruction method consists in fitting the projections of this deformable template with the segmented contours of the corresponding vertebra on the two radiographic views. The 3D reconstruction problem is stated as the minimization of a cost function for each vertebra and solved with a gradient descent technique. The reconstruction of the spine is then made vertebra by vertebra. This 3D reconstruction method has been successfully tested on several biplanar radiographic images, yielding very promising results.

[1]  J. A. de Guise,et al.  Amélioration de la technique de reconstruction 3D rapide de colonnes vertébrales scoliotiques à partir d'images radiographiques , 2000 .

[2]  Charles Kervrann,et al.  Statistical deformable model-based segmentation of image motion , 1999, IEEE Trans. Image Process..

[3]  Stéphane Lavallée,et al.  Nonrigid 3-D/2-D Registration of Images Using Statistical Models , 1999, MICCAI.

[4]  Claude Kauffmann,et al.  Digital radiography segmentation of a scoliotic vertebral body using deformable models , 1997, Medical Imaging.

[5]  J. Hadamard,et al.  Lectures on Cauchy's Problem in Linear Partial Differential Equations , 1924 .

[6]  J. A. de Guise,et al.  Techniques d'imagerie appliquées à la biomécanique rachidienne , 1995 .

[7]  Timothy F. Cootes,et al.  Training Models of Shape from Sets of Examples , 1992, BMVC.

[8]  Patrick Pérez,et al.  Hybrid Genetic Optimization and Statistical Model-Based Approach for the Classification of Shadow Shapes in Sonar Imagery , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

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

[10]  Cristian Lorenz,et al.  Generation of Point-Based 3D Statistical Shape Models for Anatomical Objects , 2000, Comput. Vis. Image Underst..

[11]  Stéphane Lavallée,et al.  Building a Complete Surface Model from Sparse Data Using Statistical Shape Models: Application to Computer Assisted Knee Surgery System , 1998, MICCAI.

[12]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Anil K. Jain,et al.  Object Matching Using Deformable Templates , 1996, IEEE Trans. Pattern Anal. Mach. Intell..