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      X-ray imaging and analysis techniques for quantifying pore-scale structure and processes in subsurface porous medium systems

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      Advances in Water Resources

      Elsevier BV

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          Most cited references 255

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          A Threshold Selection Method from Gray-Level Histograms

           Nobuyuki Otsu (1979)
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            Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.

            We make an analogy between images and statistical mechanics systems. Pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice-like physical system. The assignment of an energy function in the physical system determines its Gibbs distribution. Because of the Gibbs distribution, Markov random field (MRF) equivalence, this assignment also determines an MRF image model. The energy function is a more convenient and natural mechanism for embodying picture attributes than are the local characteristics of the MRF. For a range of degradation mechanisms, including blurring, nonlinear deformations, and multiplicative or additive noise, the posterior distribution is an MRF with a structure akin to the image model. By the analogy, the posterior distribution defines another (imaginary) physical system. Gradual temperature reduction in the physical system isolates low energy states (``annealing''), or what is the same thing, the most probable states under the Gibbs distribution. The analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations. The result is a highly parallel ``relaxation'' algorithm for MAP estimation. We establish convergence properties of the algorithm and we experiment with some simple pictures, for which good restorations are obtained at low signal-to-noise ratios.
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              Fast robust automated brain extraction.

               Teri S Krebs (2002)
              An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against "gold-standard" hand segmentations, and two other popular automated methods. Copyright 2002 Wiley-Liss, Inc.
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                Author and article information

                Journal
                Advances in Water Resources
                Advances in Water Resources
                Elsevier BV
                03091708
                January 2013
                January 2013
                : 51
                :
                : 217-246
                Article
                10.1016/j.advwatres.2012.07.018
                © 2013

                http://www.elsevier.com/tdm/userlicense/1.0/

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