Discriminative analysis of wavelets for efficient medical image compression

Deepa Sivaraman, Jeneetha Jebanazer, Bhuvaneswari Balasubramanian


Critical diagnostic information inferred using state of the artradiology techniques helps radiologists in determining the severity of diseases and hence suggest suitable treatment procedures. As a result, dealing with medical image compression necessitates a trade-off between good perceptual quality and high compression rate. The objective of this work is twofold, i) to investigate the effect of increasing the number of encoding loops on medical image compression parameters, and ii) to determine the most suitable wavelet for medical image compression. Haar, Daubechies, Biorthogonal Demeyer, Coifletand Symlet wavelets are used for comparison. Six different sets of medical images are used for testing and from the results obtained it is observed that increasing the number of encoding loops results in better compression parameters but increasing beyond 9 has no significant effect on compression parameters and thus the optimum choice for the number of encoding loops is 9. From the second analysis it is observed that changing the type of wavelets used has no significant effect on the compression parameters.


Compression ratio; Daubechies; Haar; Image compression; Mean square error

Full Text:


DOI: http://doi.org/10.11591/ijeecs.v30.i1.pp510-517


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

shopify stats IJEECS visitor statistics