A single volume element (voxel) in a medical image may be composed of a mixture of multiple tissue types. We call voxels which contain multiple tissue classes mixels. A statistical mixel image model based on Markov ra...
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A single volume element (voxel) in a medical image may be composed of a mixture of multiple tissue types. We call voxels which contain multiple tissue classes mixels. A statistical mixel image model based on Markov random field (MRF) theory and an algorithm for the classification of mixels are presented in this paper. We concentrate on the classification of multichannel magnetic resonance (MR) images of the brain although the algorithm has other applications. We also present a method for compensating for the gray-level variation of MR images between different slices, which is primarily caused by the inhomogeneity of the RF field produced by the imaging coli.
Some row-action algorithms which exploit special objective function and constraints structure have proven advantageous for solving huge and sparse feasibility or optimization problems. Recently developed block-iterati...
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Some row-action algorithms which exploit special objective function and constraints structure have proven advantageous for solving huge and sparse feasibility or optimization problems. Recently developed block-iterative versions of such special-purpose methods enable parallel computation when the underlying problem is appropriately decomposed. This opens the door for parallel computation in image reconstruction problems of computerized tomography and in the inverse problem of radiation therapy treatment planning, all in their fully discretized modelling approach. Since there is more than one way of deriving block-iterative versions of any row-action method, the choice has to be made with reference to the underlying real-world problem.
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