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International Symposium on Visual Computing

ISVC 2015: Advances in Visual Computing pp 692–701Cite as

Gaussian Processes for Slice-Based Super-Resolution MR Images

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9475))

Abstract

Magnetic resonance imaging (MRI) is a medical technique used in radiology to obtain anatomical images of healthy and pathological tissues. Due to hardware limitations and clinical protocols, MRI data are often acquired with low-resolution. For this reason, the scientific community has been developing super-resolution (SR) methodologies in order to enhance spatial resolution through post-processing of 2D multi-slice images. The enhancement of spatial resolution in magnetic resonance (MR) images improves clinical procedures such as tissue segmentation, registration and disease diagnosis. Several methods to perform SR-MR images have been proposed. However, they present different drawbacks: sensitivity to noise, high computational cost, and complex optimization algorithms. In this paper, we develop a supervised learning methodology to perform SR-MR images using a patch-based Gaussian process regression (GPR) method. We compare our approach with nearest-neighbor interpolation, B-splines and a SR-GPR scheme based on nearest-neighbors. We test our SR-GPR algorithm in MRI-T1 and MRI-T2 studies, evaluating the performance through error metrics and morphological validation (tissue segmentation). Results obtained with our methodology outperform the other alternatives for all validation protocols.

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Acknowledgments

H.D. Vargas Cardona is funded by Colciencias under the program: formación de alto nivel para la ciencia, la tecnología y la innovación - Convocatoria 617 de 2013. This research has been developed under the project: Estimación de los parámetros de neuro modulación con terapia de estimulación cerebral profunda en pacientes con enfermedad de Parkinson a partir del volumen de tejido activo planeado, financed by Colciencias with code 1110-657-40687.

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Authors and Affiliations

  1. Faculty of Engineering, Universidad Tecnológica de Pereira, Pereira, Colombia

    Hernán Darío Vargas Cardona, Andrés F. López-Lopera, Álvaro A. Orozco & Mauricio A. Álvarez

  2. Laboratorio de Análisis de Imagen Médica Y Biometría, Universidad Rey Juan Carlos, Móstoles, Spain

    Juan Antonio Hernández Tamames & Norberto Malpica

Authors
  1. Hernán Darío Vargas Cardona
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  2. Andrés F. López-Lopera
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  3. Álvaro A. Orozco
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  4. Mauricio A. Álvarez
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  5. Juan Antonio Hernández Tamames
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  6. Norberto Malpica
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Corresponding author

Correspondence to Hernán Darío Vargas Cardona .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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© 2015 Springer International Publishing Switzerland

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Cardona, H.D.V., López-Lopera, A.F., Orozco, Á.A., Álvarez, M.A., Tamames, J.A.H., Malpica, N. (2015). Gaussian Processes for Slice-Based Super-Resolution MR Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_65

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  • DOI: https://doi.org/10.1007/978-3-319-27863-6_65

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27862-9

  • Online ISBN: 978-3-319-27863-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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