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IJMLC 2020 Vol.10(3): 501-506 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.3.964

Supervoxel Clustering with a Novel 3D Descriptor for Brain Tissue Segmentation

Yongfan Liu, Sen Du and Youyong Kong

Abstract—Accurate segmentation of brain tissues from magnetic resonance imaging (MRI) is of significant importance for clinical application and scientific research. Traditional strategies to handle the 2D images have the limitation of 3D data. In this paper, to overcome these issues, a tissue segmentation approach with supervoxel clustering and the novel 3D texture extraction method are proposed. At first, the simple linear iterative clustering in three-dimension is applied, to reduce the number of calculation objects. Then, a novel local binary pattern in three-dimension is proposed for better discriminate the supervoxels with different tissues. A clustering approach is also developed to classify supervoxels with features into different types of tissues. The labels of supervoxel are finally mapped back to original data to have the tissue type of voxels. The performance of the proposed method is evaluated on the commonly utilized Internet Brain Segmentation Repository 18 dataset. The experiment showed promising results with insufficient trainset.

Index Terms—Magnetic resonance imaging, brain tissue, supervoxel, clustering, texture extraction, k-nearest neighbor.

Yongfan Liu is with Chien-shiung Wu College, Southeast University, Nanjing, China. He is now with the Division of Continuing Education, University of California, Irvine, P.O. Box 6050 USA (e-mail: yongfal@uci.edu).
Sen Du and Youyong Kong are with School of Computer Science and Engineering, Southeast University, Nanjing, China (Corresponding author: Youyong Kong; e-mail: silentchord@163.com, kongyouyong@seu.edu.cn).

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Cite: Yongfan Liu, Sen Du and Youyong Kong, "Supervoxel Clustering with a Novel 3D Descriptor for Brain Tissue Segmentation," International Journal of Machine Learning and Computing vol. 10, no. 3, pp. 501-506, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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