1 May 2007 Simultaneous reconstruction and edge detection of tomographic images using accelerated deterministic annealing
Author Affiliations +
Optical Engineering, 46(5), 057006 (2007). doi:10.1117/1.2736338
We introduce fast image reconstruction algorithms for emission tomography, which provide not only edge-preserved reconstructions, but also their edge maps simultaneously. To explicitly model the existence of edges, we use the binary line-process model, which is incorporated as a Gibbs prior in the context of a Bayesian maximum a posteriori framework. To efficiently handle the problem of mixed continuous and binary variable objectives, we use a deterministic annealing (DA) algorithm. Since the DA algorithm is computer-intensive and requires many iterations to converge, we apply a block-iterative method derived from the well-known ordered-subset principle. The block-iterative DA algorithm processes the data in blocks within each iteration, thereby accelerating the convergence speed of the standard DA algorithm by a factor proportional to the number of blocks. Our experimental results indicate that, with moderate numbers of blocks and properly chosen hyperparameters, the accelerated DA algorithm provides good reconstructions as well as edge maps with only a few iterations.
Soo-Jin Lee, "Simultaneous reconstruction and edge detection of tomographic images using accelerated deterministic annealing," Optical Engineering 46(5), 057006 (1 May 2007). http://dx.doi.org/10.1117/1.2736338

Reconstruction algorithms

Expectation maximization algorithms


Edge detection


Optical engineering

Algorithm development


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