MR Brain Image Edge Detection Guided with Distribution of Noise and Artifact

Yuchou Chang

doi:doi:10.11648/j.ajcst.20180101.11

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MR Brain Image Edge Detection Guided with Distribution of Noise and Artifact

Yuchou Chang

Published in American Journal of Computer Science and Technology (Volume 1, Issue 1)

Received: 26 October 2017 Accepted: 13 November 2017 Published: 20 December 2017

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Abstract

Magnetic resonance imaging (MRI) has revolutionized radiology in past four decades. MR image edge detection can identify anatomy boundaries and extract features for image analysis applications like segmentation and recognition of anatomy structures. Traditional MR image edge detection methods directly identify discontinuities in MR image domain without considering distribution of noise and aliasing artifact produced from MR scanner and reconstruction. It is difficult to suppress effects of noise and aliasing artifact during the edge detection process. In this project, a novel MR brain image edge detection method is proposed, which is based on parallel MRI reconstruction method. Distribution of noise and aliasing artifact is characterized by geometry factor map that also guides edge detection process for avoiding detection of noise and aliasing artifact. A collaborative learning strategy is applied on voting edges for producing the final edge detection. Experimental results show that the proposed method not only keep anatomy structure boundaries without missing edge components, but also avoid detection of noise and artifact with wrong edges.

Published in	American Journal of Computer Science and Technology (Volume 1, Issue 1)
DOI	10.11648/j.ajcst.20180101.11
Page(s)	1-7
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Edge Detection, Magnetic Resonance Imaging, Geometry Factor, Canny Edge Detector, Aliasing Artifact

References

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Yuchou Chang. (2017). MR Brain Image Edge Detection Guided with Distribution of Noise and Artifact. American Journal of Computer Science and Technology, 1(1), 1-7. https://doi.org/10.11648/j.ajcst.20180101.11

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ACS Style

Yuchou Chang. MR Brain Image Edge Detection Guided with Distribution of Noise and Artifact. Am. J. Comput. Sci. Technol. 2017, 1(1), 1-7. doi: 10.11648/j.ajcst.20180101.11

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AMA Style

Yuchou Chang. MR Brain Image Edge Detection Guided with Distribution of Noise and Artifact. Am J Comput Sci Technol. 2017;1(1):1-7. doi: 10.11648/j.ajcst.20180101.11

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@article{10.11648/j.ajcst.20180101.11,
  author = {Yuchou Chang},
  title = {MR Brain Image Edge Detection Guided with Distribution of Noise and Artifact},
  journal = {American Journal of Computer Science and Technology},
  volume = {1},
  number = {1},
  pages = {1-7},
  doi = {10.11648/j.ajcst.20180101.11},
  url = {https://doi.org/10.11648/j.ajcst.20180101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20180101.11},
  abstract = {Magnetic resonance imaging (MRI) has revolutionized radiology in past four decades. MR image edge detection can identify anatomy boundaries and extract features for image analysis applications like segmentation and recognition of anatomy structures. Traditional MR image edge detection methods directly identify discontinuities in MR image domain without considering distribution of noise and aliasing artifact produced from MR scanner and reconstruction. It is difficult to suppress effects of noise and aliasing artifact during the edge detection process. In this project, a novel MR brain image edge detection method is proposed, which is based on parallel MRI reconstruction method. Distribution of noise and aliasing artifact is characterized by geometry factor map that also guides edge detection process for avoiding detection of noise and aliasing artifact. A collaborative learning strategy is applied on voting edges for producing the final edge detection. Experimental results show that the proposed method not only keep anatomy structure boundaries without missing edge components, but also avoid detection of noise and artifact with wrong edges.},
 year = {2017}
}

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TY  - JOUR
T1  - MR Brain Image Edge Detection Guided with Distribution of Noise and Artifact
AU  - Yuchou Chang
Y1  - 2017/12/20
PY  - 2017
N1  - https://doi.org/10.11648/j.ajcst.20180101.11
DO  - 10.11648/j.ajcst.20180101.11
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 1
EP  - 7
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20180101.11
AB  - Magnetic resonance imaging (MRI) has revolutionized radiology in past four decades. MR image edge detection can identify anatomy boundaries and extract features for image analysis applications like segmentation and recognition of anatomy structures. Traditional MR image edge detection methods directly identify discontinuities in MR image domain without considering distribution of noise and aliasing artifact produced from MR scanner and reconstruction. It is difficult to suppress effects of noise and aliasing artifact during the edge detection process. In this project, a novel MR brain image edge detection method is proposed, which is based on parallel MRI reconstruction method. Distribution of noise and aliasing artifact is characterized by geometry factor map that also guides edge detection process for avoiding detection of noise and aliasing artifact. A collaborative learning strategy is applied on voting edges for producing the final edge detection. Experimental results show that the proposed method not only keep anatomy structure boundaries without missing edge components, but also avoid detection of noise and artifact with wrong edges.
VL  - 1
IS  - 1
ER  -

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Author Information

Yuchou Chang

Computer Science and Engineering Technology Department, University of Houston-Downtown, Houston, USA

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APA Style

Yuchou Chang. (2017). MR Brain Image Edge Detection Guided with Distribution of Noise and Artifact. American Journal of Computer Science and Technology, 1(1), 1-7. https://doi.org/10.11648/j.ajcst.20180101.11

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ACS Style

Yuchou Chang. MR Brain Image Edge Detection Guided with Distribution of Noise and Artifact. Am. J. Comput. Sci. Technol. 2017, 1(1), 1-7. doi: 10.11648/j.ajcst.20180101.11

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AMA Style

Yuchou Chang. MR Brain Image Edge Detection Guided with Distribution of Noise and Artifact. Am J Comput Sci Technol. 2017;1(1):1-7. doi: 10.11648/j.ajcst.20180101.11

Copy | Download

@article{10.11648/j.ajcst.20180101.11,
  author = {Yuchou Chang},
  title = {MR Brain Image Edge Detection Guided with Distribution of Noise and Artifact},
  journal = {American Journal of Computer Science and Technology},
  volume = {1},
  number = {1},
  pages = {1-7},
  doi = {10.11648/j.ajcst.20180101.11},
  url = {https://doi.org/10.11648/j.ajcst.20180101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20180101.11},
  abstract = {Magnetic resonance imaging (MRI) has revolutionized radiology in past four decades. MR image edge detection can identify anatomy boundaries and extract features for image analysis applications like segmentation and recognition of anatomy structures. Traditional MR image edge detection methods directly identify discontinuities in MR image domain without considering distribution of noise and aliasing artifact produced from MR scanner and reconstruction. It is difficult to suppress effects of noise and aliasing artifact during the edge detection process. In this project, a novel MR brain image edge detection method is proposed, which is based on parallel MRI reconstruction method. Distribution of noise and aliasing artifact is characterized by geometry factor map that also guides edge detection process for avoiding detection of noise and aliasing artifact. A collaborative learning strategy is applied on voting edges for producing the final edge detection. Experimental results show that the proposed method not only keep anatomy structure boundaries without missing edge components, but also avoid detection of noise and artifact with wrong edges.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - MR Brain Image Edge Detection Guided with Distribution of Noise and Artifact
AU  - Yuchou Chang
Y1  - 2017/12/20
PY  - 2017
N1  - https://doi.org/10.11648/j.ajcst.20180101.11
DO  - 10.11648/j.ajcst.20180101.11
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 1
EP  - 7
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20180101.11
AB  - Magnetic resonance imaging (MRI) has revolutionized radiology in past four decades. MR image edge detection can identify anatomy boundaries and extract features for image analysis applications like segmentation and recognition of anatomy structures. Traditional MR image edge detection methods directly identify discontinuities in MR image domain without considering distribution of noise and aliasing artifact produced from MR scanner and reconstruction. It is difficult to suppress effects of noise and aliasing artifact during the edge detection process. In this project, a novel MR brain image edge detection method is proposed, which is based on parallel MRI reconstruction method. Distribution of noise and aliasing artifact is characterized by geometry factor map that also guides edge detection process for avoiding detection of noise and aliasing artifact. A collaborative learning strategy is applied on voting edges for producing the final edge detection. Experimental results show that the proposed method not only keep anatomy structure boundaries without missing edge components, but also avoid detection of noise and artifact with wrong edges.
VL  - 1
IS  - 1
ER  -

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