MR Brain Image Edge Detection Guided with Distribution of Noise and Artifact
American Journal of Computer Science and Technology
Volume 1, Issue 1, March 2018, Pages: 1-7
Received: Oct. 26, 2017; Accepted: Nov. 13, 2017; Published: Dec. 20, 2017
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Author
Yuchou Chang, Computer Science and Engineering Technology Department, University of Houston-Downtown, Houston, USA
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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.
Keywords
Edge Detection, Magnetic Resonance Imaging, Geometry Factor, Canny Edge Detector, Aliasing Artifact
To cite this article
Yuchou Chang, MR Brain Image Edge Detection Guided with Distribution of Noise and Artifact, American Journal of Computer Science and Technology. Vol. 1, No. 1, 2018, pp. 1-7. doi: 10.11648/j.ajcst.20180101.11
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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