Moongu Jeon

Work place: Gwangju Institute of Science and Technology, Gwangju, Korea.



Research Interests: Computer Vision, Computational Learning Theory, Computer systems and computational processes


Moongu Jeon, received the B.E. degree in architectural engineering from the Korea University, Korea in 1988, and M.S. and Ph.D. degrees in computer science and scientific computation from the University of Minnesota, in 1999 and 2001, respectively. He was a postgraduate researcher at the University of California, Santa Barbara, from 2001 to 2003. He then joined IBD-NRC, Winnipeg, MB, Canada, have worked for two years on sparse representation and image segmentation with level set methods. In 2005 as an assistant professor he joined Gwangju Institute of Science and Technology (GIST), Korea, and now is working as a full professor there. His main research interests are in machine learning and computer vision, and he published more than 150 research papers. Also he is conducting several research projects on visual surveillance and machine learning as a principal investigator.

Author Articles
Classification of Alzheimer Disease using Gabor Texture Feature of Hippocampus Region

By Prateek Keserwani V. S. Chandrasekhar Pammi Om Prakash Ashish Khare Moongu Jeon

DOI:, Pub. Date: 8 Jun. 2016

The aim of this research is to propose a methodology to classify the subjects into Alzheimer disease and normal control on the basis of visual features from hippocampus region. All three dimensional MRI images were spatially normalized to the MNI/ICBM atlas space. Then, hippocampus region was extracted from brain structural MRI images, followed by application of two dimensional Gabor filter in three scales and eight orientations for texture computation. Texture features were represented on slice by slice basis by mean and standard deviation of magnitude of Gabor response. Classification between Alzheimer disease and normal control was performed with linear support vector machine. This study analyzes the performance of Gabor texture feature along each projection (axial, coronal and sagittal) separately as well as combination of all projections. The experimental results from both single projection (axial) as well as combination of all projections (axial, coronal and sagittal), demonstrated better classification performance over other existing method. Hence, this methodology could be used as diagnostic measure for the detection of Alzheimer disease. 

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