Search for Articles:

MECS Press Menu

V.R.Vijayakuymar

Work place: Dept. of ECE, Anna University of Technology, Coimbatore, Tamil Nadu

E-mail: vr_vijay@yahoo.com

Website:

Research Interests: Image Compression, Image Manipulation, Image Processing

Biography

Dr.V.R.Vijaykumar completed his bachelor degree in Electronics and Communication Engineering in the year 1996 from Government College of Technology Vellore and subsequently he completed his M.E. degree in Communication Systems at Thiagarajar college of Engineering Madurai in the year 1997. He completed his PhD in the area of Nonlinear Image Filtering from Anna University Chennai in the year 2008.

He has 14 years of Teaching Experience. He worked at Madras Institute of Technology as a Teaching/Research scholar during 2000-2003. Later he served at PSG College of Technology as a Lecturer/Senior Lecturer in the department of Electronics and Communication Engineering. Currently he his working as Associate Professor in the Department of Electronics and Communication Engineering, Anna University Coimbatore. His area of interest includes Image Processing, Signal Processing and Digital Communication. He has published 10 International Journal papers and more than 30 International/National Conference papers. He has edited two book chapters in the area of nonlinear image processing. Currently he is guiding 5 research scholars for Ph.D.

Author Articles
A Survey on Various Compression Methods for Medical Images

By S.Sridevi M.E V.R.Vijayakuymar R.Anuja

DOI: https://doi.org/10.5815/ijisa.2012.03.02, Pub. Date: 8 Apr. 2012

Medical image compression plays a key role as hospitals move towards filmless imaging and go completely digital. Image compression will allow Picture Archiving and Communication Systems (PACS) to reduce the file sizes on their storage requirements while maintaining relevant diagnostic information. Lossy compression schemes are not used in medical image compression due to possible loss of useful clinical information and as operations like enhancement may lead to further degradations in the lossy compression. Medical imaging poses the great challenge of having compression algorithms that reduce the loss of fidelity as much as possible so as not to contribute to diagnostic errors and yet have high compression rates for reduced storage and transmission time. This paper outlines the comparison of compression methods such as Shape-Adaptive Wavelet Transform and Scaling Based ROI,JPEG2000 Max-Shift ROI Coding, JPEG2000 Scaling-Based ROI Coding, Discrete Cosine Transform, Discrete Wavelet Transform and Subband Block Hierarchical Partitioning on the basis of compression ratio and compression quality.

[...] Read more.
Other Articles

Subscribe to receive issue release notifications and newsletters from MECS Press journals