Dr Kalavathi Palanisamy
vidwan id: 107255
Professor, Department of Computer Science and Applications
Gandhigram Rural University
Expertise
- Computer Science Information Systems
Publications
Publications
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Scopus
h-index
11
CrossRef
Citations
90
h-index
5
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Professional Recognition
National Eligibility Test for Lectureship
UGC New DelhiGold Medalist for Securing University 1st Rank
The Mother Teresa Womens UniversityUniversity 2nd Rank Holder
The Gandhigram Rural Institute (Deemed to be University)Community & Membership
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ISTE
Bio
Digital Image Processing, Medical Image Segmentation and Analysis
Personal Details
- Female
- Professor , Gandhigram Rural University
- The Gandhigram Rural Institute - Deemed to be University, Gandhigram
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Organisations (138+)
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Co-Authors (5)
M S Ramaiah University of Applied Sciences
DON BOSCO COLLEGE OF ARTS AND SCIENCE
G T N Arts College
Dr Sivagurunathan Shanmugam. A
Gandhigram Rural University
Thiagarajar College of Engineering
Scholarly Work
Development of Wavelet Based Near-Lossless 3D Medical Image Compression Technique for Telemedicine
Funding Agency: CSIR, New Delhi
Principal Investigator
1,563,812
2019 - 2024
Development of Computational Methods for Automatic Detection and Classification of Alzheimer’s Disease in Brain MR Images
Funding Agency: Department of Science and Technology, Govt. of India
Principal Investigator
2,202,000
2017 - 2021
SEGMENTATION AND QUANTIFICATION OF BRAIN TISSUE IN MAGNETIC RESONANCE IMAGING OF HUMAN HEAD SCANS FOR THE DETECTION OF ALZHEIMER'S DISEASE
LOSSLESS AND NEAR LOSSLESS COMPRESSION TECHNIQUES FOR TWO-DIMENSIONAL AND THREE-DIMENSIONAL MEDICAL IMAGES
Enhancement Techniques for IPv6 Routing Protocol for Low Power and Lossy Networks in Internet of Things
UNIFIED CEREBRAL CORTEX SEGMENTATION TECHNIQUES TO DETECT ALZHEIMER'S DISEASE USING MAGNETIC RESONANCE BRAIN IMAGES
FORTIFICATION OF CONVENTIONAL AND DEEP LEARNING METHODS FOR NEAR LOSSLESS MEDICAL IMAGE COMPRESSION USING WAVELET SUBBANDS
COHESIVE NEAR-LOSSLESS MEDICAL IMAGE COMPRESSION TECHNIQUES USING CONVOLUTIONAL AUTOENCODER BASED DEEP LEARNING MODELS FOR TELEMEDICINE
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