Abstract
The objective of image compression is to reduce of the image data in order to be able to store or transmit data in efficient form. One of the medical standards is the DICOM (Digital Imaging and Communications in Medicine) Standards Committee exists to create and maintain international standards for communication of biomedical diagnostic and therapeutic information in disciplines that use digital images and associated data. The important issues to be considered with the DICOM communication are memory requirement, bandwidth constraint and battery resource constraint.
In the proposed method, first Segmentation is applied to get two different clusters (ROI and Non ROI). Integer Wavelet Transform based compression is applied for Higher Energy clusters and JPEG is applied for another cluster. In the reconstruction part both the reconstructed clusters are fused by fusion technique. Since Lossless and lossy compression methods are used to get high compression ratio and high quality image. Efficient edge information is obtained by using Fusion.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Talukder, K.H., Harada, K.: Haar Wavelet Based Approach for Image Compression and Quality Assessment of Compressed Image. IAENG International Journal of Applied Mathematics (2007)
Lakhani, G.: Modified JPEG Huffman Coding. IEEE Trans. Image Processing 12(2), 159–169 (2003)
Wang, K., Wu, J., Pian, Z., Guo, L., Gao, L.: Edge Detection Algorithm for Magnetic Resonance Images Based on Multiscale Morphology. In: IEEE International Conference on Control and Automation, Guangzhou, China, May 30 - June 1 (2007)
Li, G., Zhang, J., Wang, Q., Hu, C., Deng, N., Li, J.: Application of Region Selective Embedded Zero tree Wavelet Coder in CT Image Compression. In: IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, September 1-4 (2005)
Miaou, S.-G., Chen, S.-T.: Automatic quality control for wavelet-based compression of volumetric medical images using distortion-constrained adaptive vector quantization. IEEE Trans.Medical Imaging 23(11) (November 2007)
Wallace, G.K.: The JPEG Still Picture Compression Standard. Communications of the ACM 34(4), 30–44 (1991)
Daubechies, I., Sweldens, W.: Factoring Wavelet Transforms into Lifting Steps. J. Fourier Analysis and Applications 4, 247–268 (1998)
Salomon’s, D.: Data Compression, 2nd edn.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Moorthi, M., Amutha, R. (2012). An Improved Algorithm for Medical Image Compression. In: Krishna, P.V., Babu, M.R., Ariwa, E. (eds) Global Trends in Information Systems and Software Applications. ObCom 2011. Communications in Computer and Information Science, vol 270. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29216-3_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-29216-3_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29215-6
Online ISBN: 978-3-642-29216-3
eBook Packages: Computer ScienceComputer Science (R0)