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Proceedings Paper

Generalized scale: theory, algorithms, and application to image inhomogeneity correction
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Paper Abstract

Scale is a fundamental concept useful in almost all image processing and analysis tasks including segmentation, filtering, interpolation, registration, visualization, and quantitative analysis. Broadly speaking, scale related work can be divided into three categories: (1) multi-scale or scale-space representation, (2) local scale, and (3) locally adaptive scale. The original formulation of scale in the form of scale-space theory came from the presence of multiple scales in nature and the desire to represent measured signals at multiple scales. However, since this representation did not suggest how to select appropriate scales, the notion of local scale was proposed to pick the right scale for a particular application from the multi-scale representation of the image. Recently, there has been considerable interest in developing locally adaptive scales, the idea being to consider the local size of object in carrying out whatever local operations that are to be done on the image. However, existing locally adaptive models are limited by shape, size, and anisotropic constraints. In this work, we propose a generalized scale model which is adaptive like other local morphometric models, and yet possesses the global spirit of multi-scale representations. We postulate that this semi-locally adaptive nature of generalized scale confers it certain distinct advantages over other global and local scale formulations. Further, generalized scale can be easily applied to solve a range of image processing problems. One such problem that we address in this paper is inhomogeneity correction in MR images. We qualitatively and quantitatively demonstrate the superiority of our generalized scale-based correction method over an existing scale-based correction technique, while retaining all the advantages of the existing scale-based method over those published in the literature.

Paper Details

Date Published: 12 May 2004
PDF: 12 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.535696
Show Author Affiliations
Anant Madabhushi, Univ. of Pennsylvania (United States)
Jayaram K. Udupa, Univ. of Pennsylvania (United States)
Andre Souza, Univ. of Pennsylvania (United States)

Published in SPIE Proceedings Vol. 5370:
Medical Imaging 2004: Image Processing
J. Michael Fitzpatrick; Milan Sonka, Editor(s)

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