An Optical Flow-Based Approach for Minimally Divergent Velocimetry Data Interpolation

An Optical Flow-Based Approach for Minimally Divergent Velocimetry Data Interpolation

Three-dimensional (3D) biomedical image sets are often acquired with in-plane pixel spacings that are far less than the out-of-plane spacings between images. The resultant anisotropy, which can be detrimental in many applications, can be decreased using image interpolation. Optical flow and/or other...

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Main Authors: Berkay Kanberoglu, Dhritiman Das, Priya Nair, Pavan Turaga, David Frakes
Format: Article
Language:English
Published: Hindawi Limited 2019-01-01
Series:International Journal of Biomedical Imaging
Online Access:http://dx.doi.org/10.1155/2019/9435163
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spelling doaj-534f19c2ee5a47a4b75d7b55c5dc323a2020-11-25T00:30:26ZengHindawi LimitedInternational Journal of Biomedical Imaging1687-41881687-41962019-01-01201910.1155/2019/94351639435163An Optical Flow-Based Approach for Minimally Divergent Velocimetry Data InterpolationBerkay Kanberoglu0Dhritiman Das1Priya Nair2Pavan Turaga3David Frakes4School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, 85281, USADepartment of Computer Science, Technical University of Munich, Munich, 80333, GermanySchool of Biological and Health Systems Engineering, Arizona State University, Tempe, 85281, USASchool of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, 85281, USASchool of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, 85281, USAThree-dimensional (3D) biomedical image sets are often acquired with in-plane pixel spacings that are far less than the out-of-plane spacings between images. The resultant anisotropy, which can be detrimental in many applications, can be decreased using image interpolation. Optical flow and/or other registration-based interpolators have proven useful in such interpolation roles in the past. When acquired images are comprised of signals that describe the flow velocity of fluids, additional information is available to guide the interpolation process. In this paper, we present an optical-flow based framework for image interpolation that also minimizes resultant divergence in the interpolated data.http://dx.doi.org/10.1155/2019/9435163
collection DOAJ
language English
format Article
sources DOAJ
author Berkay Kanberoglu
Dhritiman Das
Priya Nair
Pavan Turaga
David Frakes
spellingShingle Berkay Kanberoglu
Dhritiman Das
Priya Nair
Pavan Turaga
David Frakes
An Optical Flow-Based Approach for Minimally Divergent Velocimetry Data Interpolation
International Journal of Biomedical Imaging
author_facet Berkay Kanberoglu
Dhritiman Das
Priya Nair
Pavan Turaga
David Frakes
author_sort Berkay Kanberoglu
title An Optical Flow-Based Approach for Minimally Divergent Velocimetry Data Interpolation
title_short An Optical Flow-Based Approach for Minimally Divergent Velocimetry Data Interpolation
title_full An Optical Flow-Based Approach for Minimally Divergent Velocimetry Data Interpolation
title_fullStr An Optical Flow-Based Approach for Minimally Divergent Velocimetry Data Interpolation
title_full_unstemmed An Optical Flow-Based Approach for Minimally Divergent Velocimetry Data Interpolation
title_sort optical flow-based approach for minimally divergent velocimetry data interpolation
publisher Hindawi Limited
series International Journal of Biomedical Imaging
issn 1687-4188
1687-4196
publishDate 2019-01-01
description Three-dimensional (3D) biomedical image sets are often acquired with in-plane pixel spacings that are far less than the out-of-plane spacings between images. The resultant anisotropy, which can be detrimental in many applications, can be decreased using image interpolation. Optical flow and/or other registration-based interpolators have proven useful in such interpolation roles in the past. When acquired images are comprised of signals that describe the flow velocity of fluids, additional information is available to guide the interpolation process. In this paper, we present an optical-flow based framework for image interpolation that also minimizes resultant divergence in the interpolated data.
url http://dx.doi.org/10.1155/2019/9435163
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