V. S. Chandrasekhar Pammi

Work place: Centre of Behavioural and Cognitive Sciences (CBCS), University of Allahabad, Allahabad, India.

E-mail: cpammi@cbcs.ac.in


Research Interests: Models of Computation, Computer systems and computational processes, Computational Engineering, Computational Science and Engineering


V. S. Chandrasekhar Pammi is a Professor in Centre of Behavioural and Cognitive Sciences (CBCS), University of Allahabad, Allahabad (U.P.), INDIA. He has completed Ph.D. in Computer Science from Department of Computer and Information Sciences, University of Hyderabad, Hyderabad, India in 2005. Before joining Allahabad University, he worked at several research institutes, viz., Max Plank Institute for Biological Cybernetics, Tuebingen, Germany, Emory University, Atlanta, USA and ATR Labs, Kyoto, Japan. His research interest includes Cognitive and Computational Neuroscience aspects of Decision Making, Sequential skill learning, Cross-modal Integration.

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: https://doi.org/10.5815/ijigsp.2016.06.02, 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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