A central reconstruction based strategy for selecting projection angles in binary tomography
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Dynamic angle selection in binary tomography
Computer Vision and Image Understanding
A method for feature detection in binary tomography
DGCI'13 Proceedings of the 17th IAPR international conference on Discrete Geometry for Computer Imagery
Bounds on the quality of reconstructed images in binary tomography
Discrete Applied Mathematics
Discrete Tomography in MRI: a Simulation Study
Fundamenta Informaticae - Strategies for Tomography
Approximate Discrete Reconstruction Algorithm
Fundamenta Informaticae - Strategies for Tomography
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In this paper, we present an iterative reconstruction algorithm for discrete tomography, called discrete algebraic reconstruction technique (DART). DART can be applied if the scanned object is known to consist of only a few different compositions, each corresponding to a constant gray value in the reconstruction. Prior knowledge of the gray values for each of the compositions is exploited to steer the current reconstruction towards a reconstruction that contains only these gray values. Based on experiments with both simulated CT data and experimental μCT data, it is shown that DART is capable of computing more accurate reconstructions from a small number of projection images, or from a small angular range, than alternative methods. It is also shown that DART can deal effectively with noisy projection data and that the algorithm is robust with respect to errors in the estimation of the gray values.