Curve evolution object-based techniques for image reconstruction and segmentation
Curve evolution object-based techniques for image reconstruction and segmentation
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation
International Journal of Computer Vision
Multi-Reference Shape Priors for Active Contours
International Journal of Computer Vision
Deterministic edge-preserving regularization in computed imaging
IEEE Transactions on Image Processing
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Regularized pixel-based tomographic reconstruction techniques suffer from streaking artifacts when only few projection angles are available. Shape-based methods, that reconstruct objects by optimizing their boundaries typically enforce a length penalty on the evolving curve, which is not suited to all possible shapes or topologies. To overcome this limitation, we propose in this paper to introduce high-level shape priors in tomographic reconstruction using active contours. Our shape descriptor is moment-based - hence rather compact and hierarchical - and may be made invariant to geometric transformations up to affine ones. It can handle multiple references simultaneously to accommodate shape variations. Experimental results on synthetic data show the effectiveness of the prior, especially for small numbers of noisy projections.