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Proceedings Paper

Noise-resistant adaptive scale using stabilized diffusion
Author(s): Andre Souza
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Paper 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.

Paper Details

Date Published: 15 March 2011
PDF: 11 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79624W (15 March 2011); doi: 10.1117/12.871348
Show Author Affiliations
Andre Souza, Carestream Health, Inc. (United States)


Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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