Systems, Man & Cybernetics - April 2017 - 22

Conclusion
In this work, we focused on developing an automated framework using
structured kernel sparse representation to deal with the challenging multilabel brain tumor segmentation
task. The sparse property of medical
images was leveraged in our study,
and results showed the effectiveness
of our method. We plan to further
research the power of sparsity in our
future works for medical image analysis and volume visualization.

In the training phase,
kernel-dictionary
learning leads
to class-specific
dictionaries that are
optimized for each
task-relevant class.

About the Authors
Xuan Chen (xuan.chen@u.nus.edu) earned her B.E. degree
in communication engineering from Beijing University of
Posts and Telecommunications, China, in 2014. She is currently pursuing her Ph.D. degree in the Department of Electrical and Computer Engineering at the National University
of Singapore. Her research interests include medical image
analysis, visualization, and machine learning.
Binh P. Nguyen (phubinh@ieee.org) earned his B.Eng.
degree in information technology in 2002 and his M.Sc.
degree in information processing and communications in
2004 from Hanoi University of Science and Technology, Vietnam, where he was also a lecturer in the School of Information and Communication Technology. He earned his Ph.D.
degree in the Department of Electrical and Computer Engineering at the National University of Singapore. After
obtaining his Ph.D. degree, he worked as a research fellow
in the Department of Mechanical Engineering at the
National University of Singapore and then as a research
fellow in the Centre for Computational Biology, DukeNational University of Singapore Medical School, Singapore. Currently, he is a scientist at the Institute of High
Performance Computing, Agency for Science, Technology
and Research, Singapore. His research interests include
medical image analysis and visualization, health data sciences and informatics, computational intelligence and
interdisciplinary computing, multimedia analysis, and human-
machine systems.
Chee-Kong Chui (mpecck@nus.edu.sg) earned his
Ph.D. degree from the University of Tokyo, Japan. He is an
associate professor with the Control and Mechatronics
Division of the Department of Mechanical Engineering at
the National University of Singapore, where his group
focuses on medical devices, robotics, imaging, and simulations. His current focus on immersive media involves the
provision of visual and haptic cues to assist humans in the
training of hand-eye coordination and to augment hand-
eye coordination in a mixed-reality environment. He is a
Senior Member of the IEEE and was chairman of the IEEE
Engineering in Medicine and Biology Society Singapore
Chapter (2015-2016).

22

IEEE SyStEmS, man, & CybErnEtICS magazInE A pri l 2017

Sim-Heng Ong (eleongsh@nus.
edu.sg) earned his B.E. degree from
the University of Western Australia
and his Ph.D. degree from the University of Sydney, Australia. He is
an associate professor in the
Department of Electrical and Computer Engineering at the National
University of Singapore. His re search interests are in signal and
image processing. He has published
extensively in international journals
and conference proceedings.

References
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http://www.edu.sg http://www.M.Sc
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