American Journal of Applied Sciences

An Efficient Low Complexity Lossless Coding Algorithm for Medical Images

S. E. Ghrare, M.A. Mohd. Ali, K. Jumari and M. Ismail

DOI : 10.3844/ajassp.2009.1502.1508

American Journal of Applied Sciences

Volume 6, Issue 8

Pages 1502-1508


Problem statement: Nowadays a large number of various medical images are generated from hospitals and medical centers with sophisticated image acquisition devices, the movement toward digital images in radiology presents the problem of how to conveniently and economically store, retrieve and transmit the volume of digital images. Thus digital image data compression is necessary in order to solve this problem. So in a wide range of medical applications such as disease diagnostic and during the compression process, the loss of information is unacceptable; hence medical images are required to be at high resolution as possible. Instead of lossy compression with relatively high compression ratio, mathematical lossless compression methods are favored in this field. Approach: In this study, an efficient new lossless image coding algorithm using a simple technique was presented. Our coding algorithm was based on pixel redundancy reduction by formulating two matrices only, which were Gray Scale Matrix (GSM) and Binary Matrix (BM). These matrices had been used for coding and decoding processes. Results: Results showed that the maximum compression ratio achieved using the proposed method was 4:1, which was more efficient than the present lossless techniques, moreover the computational complexity is greatly simplified; therefore producing very fast coding and decoding. Conclusion: This algorithm was most suitable for those images where lossy compression was avoided such as medical images used for teleradiology and other telemedicine purposed and it can be applied to other medical modalities.


© 2009 S. E. Ghrare, M.A. Mohd. Ali, K. Jumari and M. Ismail. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.