15 March 2011 Noise-resistant adaptive scale using stabilized diffusion
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Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 79624W (2011) https://doi.org/10.1117/12.871348
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States
Abstract
Semi-locally adaptive models have appeared in medical imaging literature in the past years. In particular, generalized scale models (or g-scale for short) have been introduced to effectively overcome the shape, size, or anisotropic constraints imposed by previous local morphometric scale models. The g-scale models have shown interesting theoretical properties and an ability to drive improved image processing as shown in previous works. In this paper, we present a noise-resistant variant for g-scale set formation, which we refer to as stabilized scale (s-scale) because of its stabilized diffusive properties. This is a modified diffusion process wherein a well-conditioned and stable behavior in the vicinity of boundaries is defined. Yet, s-scale includes an intensity-merging dynamics behavior in the same manner as that found in the switching control of a nonlinear system. Basically we introduce, in the evolution of the diffusive model, a behavior state to drive neighboring voxel intensities to larger and larger iso-intensity regions. In other words, we drive our diffusion process to a coarser and coarser piecewise-constant approximation of the original scene. This strategy reveals a well-known behavior in control theory, called sliding modes. Evaluations on a mathematical phantom, the Brainweb, MR and CT data sets were conducted. The s-scale has shown better performance than the original g-scale under moderate to high noise levels.
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Andre Souza, Andre Souza, } "Noise-resistant adaptive scale using stabilized diffusion", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79624W (15 March 2011); doi: 10.1117/12.871348; https://doi.org/10.1117/12.871348
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