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SIAM Journal on Discrete Mathematics
Markov random field modeling in computer vision
Markov random field modeling in computer vision
The discrete Radon transform and its approximate inversion via linear programming
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Mathematical Programming: Series A and B
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Journal of the ACM (JACM)
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximating Binary Images from Discrete X-Rays
SIAM Journal on Optimization
Integer Multicommodity Flows with Reduced Demands
ESA '93 Proceedings of the First Annual European Symposium on Algorithms
Segmentation by Grouping Junctions
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Energy Minimization via Graph Cuts: Settling What is Possible
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Approximation algorithms for covering/packing integer programs
Journal of Computer and System Sciences
Discrete tomography by convex-concave regularization and D.C. programming
Discrete Applied Mathematics - Special issue: IWCIA 2003 - Ninth international workshop on combinatorial image analysis
Convergence of the simultaneous algebraic reconstruction technique (SART)
IEEE Transactions on Image Processing
Research of the image reconstruction from incomplete projection data based on the homotopy mapping
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 3
Sparse angular CT reconstruction using non-local means based iterative-correction POCS
Computers in Biology and Medicine
A novel kernel-based limited-view computerized tomography reconstruction via anisotropic diffusion
Computers and Electrical Engineering
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This paper proposes a new discrete optimization framework for tomographic reconstruction and segmentation of CT volumes when only a few projection views are available. The problem has important clinical applications in coronary angiographic imaging. We first show that the limited view reconstruction and segmentation problem can be formulated as a 'constrained' version of the metric labeling problem. This lays the groundwork for a linear programming framework that brings metric labeling classification and classical algebraic tomographic reconstruction (ART) together in a unified model. If the imaged volume is known to be comprised of a finite set of attenuation coefficients (a realistic assumption), given a regular limited view reconstruction, we view it as a task of voxels reassignment subject to maximally maintaining consistency with the input reconstruction and the objective of ART simultaneously. The approach can reliably reconstruct (or segment) volumes with several multiple contrast objects. We present evaluations using experiments on cone beam computed tomography.