Generalized ball-scale: theory, algorithms, and application to image inhomogeneity correction

Anant Madabhushi; Jayaram K. Udupa; Andre Souza

doi:10.1117/12.595360

29 April 2005 Generalized ball-scale: theory, algorithms, and application to image inhomogeneity correction

Anant Madabhushi, Jayaram K. Udupa, Andre Souza

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Proceedings Volume 5747, Medical Imaging 2005: Image Processing; (2005) https://doi.org/10.1117/12.595360
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

The concept of generalized scale (g-scale) was introduced previously to overcome the shape, topological, and anisotropic constraints imposed by previous local morphometric scale models. Roughly speaking, the g-scale of a voxel in a scene was defined as the largest set of all voxels associated with it, that satisfy some homogeneity criterion. g-scale was shown to have interesting theoretical properties, and its superiority to an existing image background inhomogeneity correction method was demonstrated. In this paper, we present a variant of g-scale that we refer to as g_B-scale. The difference between g- and g_B-scale is that, while for g-scale, individual voxels are included into the g-scale set one at a time, the g_B-scale set is grown by including hyperballs, the hyperball corresponding to the local ball scale at every voxel (which, briefly, is the radius of the largest hyperball of homogeneous intensity centered at the voxel). The g_B-scale model was found to be more resistant to severe levels of inhomogeneity and noise compared to g-scale. A methodology to perform image background inhomogeneity correction based on the idea of g_B-scale was qualitatively and quantitatively compared on nearly 250 clinical and phantom datasets, with a ball-scale- and a g-scale-based correction methodology. For scenes containing inhomogeneity but no noise, the g-, and g_B-scale methods performed comparably and were superior to the ball-scale method. For scenes containing both noise and inhomogeneity, the g_B-scale-based method outperformed both the g- and ball-scale correction methods.

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Anant Madabhushi, Jayaram K. Udupa, and Andre Souza "Generalized ball-scale: theory, algorithms, and application to image inhomogeneity correction", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); https://doi.org/10.1117/12.595360

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KEYWORDS

Image processing

Magnetic resonance imaging

Binary data

3D modeling

Image segmentation

Algorithms

Brain

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