Introduction to the theory of neural computation
Introduction to the theory of neural computation
Principles of computerized tomographic imaging
Principles of computerized tomographic imaging
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
An evolutionary algorithm for discrete tomography
Discrete Applied Mathematics - Special issue: IWCIA 2003 - Ninth international workshop on combinatorial image analysis
A coordinate ascent approach to tomographic reconstruction of label images from a few projections
Discrete Applied Mathematics - Special issue: IWCIA 2003 - Ninth international workshop on combinatorial image analysis
Discrete tomography by convex-concave regularization and D.C. programming
Discrete Applied Mathematics - Special issue: IWCIA 2003 - Ninth international workshop on combinatorial image analysis
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Tomography deals with the reconstruction of the density distribution inside an unknown object from its projections in several directions. In Discrete tomography one focuses on the reconstruction of objects having a small, discrete set of density values. Using this prior knowledge in the reconstruction algorithm may vastly reduce the number of projections that is required to obtain high quality reconstructions. Recently the first generation of real-time tomographic scanners has appeared, capable of acquiring several images per second. Discrete tomography is well suited for real-time operation, as only few projections are required, reducing scanning time. However, for efficient real-time operation an extremely fast reconstruction algorithm is also required. In this paper we present a new reconstruction method, which is based on a feed-forward neural network. The network can compute reconstructions extremely fast, making it suitable for real-time tomography. Our experimental results demonstrate that the approach achieves good reconstruction quality.