WITHDRAWN: A Novel Robust Local Anisotropic Clustering Model for Tissue Segmentation and Bias Field Correction of Brain MR Image

Authors

DOI:

https://doi.org/10.3991/ijoe.v15i06.9631

Keywords:

Image segmentation, Brain MR image, Energy minimization, Anisotropic weighting, Bias field correction, Intensity inhomogeneity

Abstract


This paper had been withdrawn by the authors, but has been inadvertently published by iJOE. It is hereby officially deleted from the issue.

The International Journal of Online and Biomedical Engineering apologizes to the authors for the trouble caused.

Author Biographies

Zhe Zhang, Heilongjiang University, Harbin, China

Zhe Zhang received the B.S. Degree in communication engineering from Heilongjiang University, Harbin, China in 2017.He is a member of the Advance Ubiquitous Networking Communication Laboratory. He is a Post-Graduate Fellow with the school of Electronics Engineering, Heilongjiang University, Harbin, China. His main research includes medical image analysis and computer vision.

Jianhua Song, Minnan Normal University, Zhangzhou, Chinaï¼›Heilongjiang University, Harbin, China;

Jianhua Song received the B.S. degree in electronic information engineering from Heilongjiang University, Harbin, China in 2002, and the M.S. degree in signal and information processing and Ph.D. degree in control science and engineering from the Harbin Engineering University in 2009 and 2017, respectively. He is currently an associated professor in college of physics and information engineering of Minnan Normal University. His research interests include medical image analysis and processing, pattern recognition, intelligent algorithm design and analysis.

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Published

2019-03-29

How to Cite

Zhang, Z., & Song, J. (2019). WITHDRAWN: A Novel Robust Local Anisotropic Clustering Model for Tissue Segmentation and Bias Field Correction of Brain MR Image. International Journal of Online and Biomedical Engineering (iJOE), 15(06), pp. 15–30. https://doi.org/10.3991/ijoe.v15i06.9631

Issue

Vol. 15 No. 06 (2019)

Section

Papers

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