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Edge Detection from Non-Uniform Fourier Data via a Modified Method of Convolutional Gridding

Abstract The recovery of edge information in the physical domain from non-uniform Fourier data is of importance in a variety of applications, particularly in the practice of magnetic resonance imaging (MRI). Edge detection can be important as a goal in and of itself in the identification of tissue boundaries such as those defining the locations of tumors. It can also be an invaluable tool in the amelioration of the negative effects of the Gibbs phenomenon on reconstructions of functions with discontinuities or images in multi-dimensions with internal edges. In this thesis we develop a novel method for recovering edges from non-uniform Fourier data by adapting the "convolutional gridding" method of function reconstruction. We analyze the be... (more)
Created Date 2013-05
Contributor Martinez, Adam (Author) / Gelb, Anne (Thesis Director) / Cochran, Douglas (Committee Member) / Platte, Rodrigo (Committee Member) / Barrett, The Honors College / School of Mathematical and Statistical Sciences
Subject MRI / Convolutional Gridding / Edge Detection / Nonuniform Fourier Data
Series Academic Year 2012-2013
Type Text
Extent 33 pages
Language English
Reuse Permissions All Rights Reserved
Collaborating Institutions Barrett, the Honors College
Additional Formats MODS / OAI Dublin Core / RIS

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