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Improving digital signal interpolation: L2-optimal kernels with kernel-invariant interpolation speed
Interpolation is responsible for digital signal resampling and can signi...
04/21/2011 ∙ by Oleg S. Pianykh, et al. ∙
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Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges
Magnetic Resonance Imaging (MRI) suffers from several artifacts, the mos...
12/05/2019 ∙ by Jeffrey Ma, et al. ∙
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MIINet: An Image Quality Improvement Framework for Supporting Medical Diagnosis
Medical images have been indispensable and useful tools for supporting m...
11/28/2020 ∙ by Quan Huu Cap, et al. ∙
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A GMM based algorithm to generate point-cloud and its application to neuroimaging
Recent years have witnessed the emergence of 3D medical imaging techniqu...
11/05/2019 ∙ by Liu Yang, et al. ∙
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Distributed Object Medical Imaging Model
Digital medical informatics and images are commonly used in hospitals to...
10/09/2009 ∙ by Ahmad Shukri Mohd Noor, et al. ∙
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Accelerating B-spline Interpolation on GPUs: Application to Medical Image Registration
Background and Objective. B-spline interpolation (BSI) is a popular tech...
04/13/2020 ∙ by Orestis Zachariadis, et al. ∙
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A Community-Driven Validation Service for Standard Medical Imaging Objects
Digital medical imaging laboratories contain many distinct types of equi...
06/23/2018 ∙ by Jorge Miguel Silva, et al. ∙
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L2-optimal image interpolation and its applications to medical imaging
06/11/2010 ∙ by Oleg Pianykh, et al. ∙
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Digital medical images are always displayed scaled to fit particular view. Interpolation is responsible for this scaling, and if not done properly, can significantly degrade diagnostic image quality. However, theoretically-optimal interpolation algorithms may also be the most time-consuming and impractical. We propose a new approach, adapted to the needs of digital medical imaging, to combine high interpolation speed and superior L2-optimal image quality.
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