Abstract
We present an approach for controlling relaxation parameters in variable conductance diffusion. This approach incorporates a Bayesian classifier to perform a partial labeling of an image, followed by a diffusion step. Conductance values between pixels are controlled by statistical measurements made of the partial classification. Several iterations follow, interleaving partial labeling with diffusion steps until a convergence or stopping criterion is met. The method is suitable for performing diffusion within multi-valued images. It consistently controls relaxation parameters, even in the presence of noise. The method is presented along with results on phantom and MR images.
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© 1993 Springer-Verlag Berlin Heidelberg
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Yoo, T.S., Coggins, J.M. (1993). Using statistical pattern recognition techniques to control variable conductance diffusion. In: Barrett, H.H., Gmitro, A.F. (eds) Information Processing in Medical Imaging. IPMI 1993. Lecture Notes in Computer Science, vol 687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0013805
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DOI: https://doi.org/10.1007/BFb0013805
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