Titlebook: Discrete Tomography; Foundations, Algorit Gabor T. Herman,Attila Kuba Book 1999 Springer Science+Business Media New York 1999 3-D torus.bay

avulsion

Ancillary

Cerebral Arteriovenous Malformations,roblem of the reconstruction of characteristic functions (i.e., functions with values 0 and 1). Necessary and sufficient conditions are given to decide the existence and the uniqueness of a two-valued function if its projections and the two values are known. These conditions can also be applied if t

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Quyen D. Chu,John F. Gibbs,Gazi B. Zibaris. It has some interesting applications in image processing, electron microscopy, statistical data security, biplane angiography and graph theory. This chapter presents the computational complexity results of the problem of reconstructing a set from its horizontal and vertical projections with respe

Seizure

dysphagia

Locally Advanced Breast Cancer (LABC)es in that the formulation is suited for the modeling of discrete images, and hence readily applicable to discrete tomography problems. Second, the distribution is “image-modeling” in the sense that random samples drawn from the distribution are likely to share important characteristics of the image

貧困

Biology of Cancer Invasion and Metastasisptimization to find that reconstruction. Multiscale models have succeeded in improving representation of structure of varying scale in imagery, a chronic problem for common Markov random fields. This chapter shows that associated multiscale methods of optimization also avoid local minima of the log

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The Biologic Basis of Cancer Chemotherapyndered their direct application to fully three-dimensional binary problems. This chapter proposes a steering scheme by which non-binary iterative reconstruction algorithms can be steered towards a binary solution of a binary problem. Experimental studies show the viability of this approach.

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Scott R. Schell,Robert Udelsmandered. The approach is based on (a) relaxing the binary constraints.or 1 to interval constraints .,and (b) applying a minimum distance method (using Kullback-Leibler’s information divergence index as our distance function) to find such an . — say, .— for which the distance between the observed and t

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