Improved convergence of gradient-based reconstruction using multi-scale models

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Geometric models have received increasing attention in medical imaging for tasks such as segmentation, reconstruction, restoration, and registration. In order to determine the best configuration of the geometric model in the context of any of these tasks, one needs to perform a difficult global optimization of an energy function that may have many local minima. Explicit models of geometry, also called deformable models, snakes, or active contours, have been used extensively to solve image segmentation problems in a non-Bayesian framework. Researchers have seen empirically that multi-scale analysis is useful for convergence to a configuration that is near the global minimum. ... continued below

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12 p.

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Cunningham, G.S.; Hanson, K.M. & Koyfman, I. May 1, 1996.

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Description

Geometric models have received increasing attention in medical imaging for tasks such as segmentation, reconstruction, restoration, and registration. In order to determine the best configuration of the geometric model in the context of any of these tasks, one needs to perform a difficult global optimization of an energy function that may have many local minima. Explicit models of geometry, also called deformable models, snakes, or active contours, have been used extensively to solve image segmentation problems in a non-Bayesian framework. Researchers have seen empirically that multi-scale analysis is useful for convergence to a configuration that is near the global minimum. In this type of analysis, the image data are convolved with blur functions of increasing resolution, and an optimal configuration of the snake is found for each blurred image. The configuration obtained using the highest resolution blur is used as the solution to the global optimization problem. In this article, the authors use explicit models of geometry for a variety of Bayesian estimation problems, including image segmentation, reconstruction and restoration. The authors introduce a multi-scale approach that blurs the geometric model, rather than the image data, and show that this approach turns a global, highly nonquadratic optimization into a sequence of local, approximately quadratic problems that converge to the global minimum. The result is a deterministic, robust, and efficient optimization strategy applicable to a wide variety of Bayesian estimation problems in which geometric models of images are an important component.

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12 p.

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INIS; OSTI as DE96009246

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  • Society of Photo-Optical Instrumentation Engineers (SPIE) conference on medical imaging, Newport Beach, CA (United States), 10-15 Feb 1996

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  • Other: DE96009246
  • Report No.: LA-UR--96-1202
  • Report No.: CONF-960219--3
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 231595
  • Archival Resource Key: ark:/67531/metadc670448

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • May 1, 1996

Added to The UNT Digital Library

  • June 29, 2015, 9:42 p.m.

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  • March 1, 2016, 5:26 p.m.

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Cunningham, G.S.; Hanson, K.M. & Koyfman, I. Improved convergence of gradient-based reconstruction using multi-scale models, article, May 1, 1996; New Mexico. (digital.library.unt.edu/ark:/67531/metadc670448/: accessed November 22, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.