Fourier Descritpor-Based Deformable Models for Segmentation of the Distal Femur in CT
Abstract
Anatomical shapes present a unique problem in terms of accurate representation and medical image segmentation. Three-dimensional statistical shape models have been extensively researched as a means of autonomously segmenting and representing models. We present a segmentation method driven by a statistical shape model based on a priori shape information from manually segmented training image sets. Our model is comprised of a stack of two-dimensional Fourier descriptors computed from the perimeters of the segmented training image sets after a transformation into a canonical coordinate frame. Our segmentation process alternates between a local active contour process and a projection onto a global PCA basis of the statistical shape model. We apply our method to the segmentation of CT and MRI images of the distal femur and show quantitatively that it recovers bone shape more accurately from real imagery than a recently published method recovers bone shape from synthetically segmented imagery.
Keywords
automatic 3D image segmentation Fourier shape descriptors principal components analysis statistical shape model active contours snakesPreview
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1.5 References
- [1]T. Cootes and C. Taylor, “Active shape models-'smart snakes',” Proc. British Mach. Vision Conf., pp. 266–275, 1992.Google Scholar
- [2]C. Taylor, T. Cootes, A. Hill, and J. Haslam, “Medical Image Segmentation Using Active Shape Models,” Proc. Medical Imaging Workshop, Brusseles, Belgium, pp. 121–143, 1995.Google Scholar
- [3]M. Kaus, V. Pekar, C. Lorenz, R. Truyen, S. Lobregt, and J. Weese, “Automated 3-D PDM Construction from Segmented Images Using Deformable Models,” IEEE Transactions on Medical Imaging, Vol. 22, No. 8, pp. 1005–1013, Aug 2003.CrossRefGoogle Scholar
- [4]M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models,” Int. J. Comput. Vis., vol. 1, pp. 321–331, 1987.CrossRefGoogle Scholar
- [5]L. Staib and J. Duncan, “Boundary Finding with Parametrically Deformable Models,” IEEE PAMI, Vol. 14, No. 11, pp. 1061–1075, Nov 1992.Google Scholar
- [6]C. Zahn and R. Roskies, “Fourier Descriptors for Plane Closed Curves,” IEEE Transactions on Computers, Vol. 21, No. 3, pp. 269–281, Mar 1972.MathSciNetCrossRefMATHGoogle Scholar
- [7]E. Persoon and K. Fu, “Shape Discrimination Using Fourier Descriptors,” IEEE Trans, on Sys., Man, and Cyber., vol. SMC-7, no. 3, pp. 629–639, Mar 1977.MathSciNetGoogle Scholar
- [8]T. Cootes, A. Hill, C. Taylor, and J. Haslam, “The Use of Active Shape Models for Locating Structures in Medical Images,” Image and Vision Computing, vol. 12, no. 6, pp. 355–365, Jul 1994.CrossRefGoogle Scholar
- [9]T. Cootes, G. Edwards, and C. Taylor, “Active appearance models,” in Proc. Eur. Conf. Computer Vision, vol. 2, H. Burkhardt and B. Neumann, Eds, pp. 484–498, 1998.Google Scholar
- [10]T. Hutton, B. Buxton, P. Hammond, and H. Potts, “Estimating Average Growth trajectories in Shape-Space Using Kernel Smoothing,” IEEE Transactions on Medical Imaging, Vol. 22, No. 6, pp. 747–753, Jun 2003.CrossRefGoogle Scholar
- [11]A. Kelemen, G. Székely, and G. Gerig, “Elastic Model-Based Segmentation of 3-D Neuroradiological Data Sets,” IEEE Transactions on Medical Imaging, Vol. 18, No. 10, pp. 828–839, Oct 1999.CrossRefGoogle Scholar