Om Prakash

Work place: Centre of Computer Education, Institute of Professional Studies, University of Allahabad, Allahabad, India



Research Interests: Data Structures and Algorithms, Computer Vision, Computer systems and computational processes


Om Prakash received M.Sc. and D.Phil. (Computer Science) degrees from University of Allahabad, India in 2002 and 2014 respectively. He has been involved in teaching and research for more than 11 years. He has worked as a Post-Doc Researcher, in Gwangju Institute of Science and Technology (GIST), South Korea. Presently he is working in Institute of Professional Studies, University of Allahabad, Allahabad, INDIA. He has published over 20 papers in International journals and conference proceedings. His research interests include image and video processing, computer vision, wavelet transforms and multisensory data fusion.

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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