Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
A variational level set approach to multiphase motion
Journal of Computational Physics
The fast construction of extension velocities in level set methods
Journal of Computational Physics
Computer Vision and Image Understanding
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In this paper a fully automated segmentation system for the femur in the knee in Magnetic Resonance Images and the brain in Single Photon Emission Computed Tomography images is presented. To do this several data sets were first segmented manually. The resulting structures were represented by unorganised point clouds. With level set methods surfaces were fitted to these point clouds. The iterated closest point algorithm was then applied to establish correspondences between the different surfaces. Both surfaces and correspondences were used to build a three dimensional statistical shape model. The resulting model is then used to automatically segment structures in subsequent data sets through three dimensional Active Shape Models. The result of the segmentation is promising, but the quality of the segmentation is dependent on the initial guess.