Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Level-Set Approach to 3D Reconstruction from Range Data
International Journal of Computer Vision
A Curve Evolution Approach to Medical Image Magnification via the Mumford-Shah Functional
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Fundamentals of Computerized Tomography: Image Reconstruction from Projections
Fundamentals of Computerized Tomography: Image Reconstruction from Projections
Vis files: computational field visualization
ACM SIGGRAPH Computer Graphics
The Telescience Portal for advanced tomography applications
Journal of Parallel and Distributed Computing - Special issue on computational grids
Open source software for medical image processing and visualization
Communications of the ACM - Medical image modeling
Photo-Consistent Reconstruction of Semitransparent Scenes by Density-Sheet Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICC'09 Proceedings of the 13th WSEAS international conference on Circuits
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Under ideal circumstances the problem of tomographic reconstruction is well-posed, and measured data are sufficient to obtain accurate estimates of volume densities. In such cases segmentation and surface estimation from the reconstructed volume are justified. In other situations the reconstructed volumes are not suitable for subsequent segmentation. This can happen in the case of incomplete sinograms, noise in the measurement process, or misregistration of the views. This paper presents a direct approach to the segmentation of incomplete and noisy tomographic data. The strategy is to impose a fairly simple model on the data, and treat segmentation as a problem of estimating the interface between two substances of somewhat homogeneous density. The segmentation is achieved by simultaneously deforming a surface model and updating density parameters in order to achieve a best fit between the projected model and the input sinograms. The deformation is implemented with level-set surface models, calculated at the resolution of the input data. Several computational innovations make the approach feasible with state-of-the-art computers. The usefulness of the approach is demonstrated by reconstructing the shape of spiny dendrites from electron microscope tomographic data.