Quality Evaluation of Compressed MRI Medical Images for Telemedicine Applications
Medical image modalities such as computed
tomography (CT), magnetic resonance imaging (MRI), ultrasound
(US), X-ray are adapted to diagnose disease. These modalities
provide flexible means of reviewing anatomical cross-sections and
physiological state in different parts of the human body. The raw
medical images have a huge file size and need large storage
requirements. So it should be such a way to reduce the size of those
image files to be valid for telemedicine applications. Thus the image
compression is a key factor to reduce the bit rate for transmission or
storage while maintaining an acceptable reproduction quality, but it is
natural to rise the question of how much an image can be compressed
and still preserve sufficient information for a given clinical
application. Many techniques for achieving data compression have
been introduced. In this study, three different MRI modalities which
are Brain, Spine and Knee have been compressed and reconstructed
using wavelet transform. Subjective and objective evaluation has
been done to investigate the clinical information quality of the
compressed images. For the objective evaluation, the results show
that the PSNR which indicates the quality of the reconstructed image
is ranging from (21.95 dB to 30.80 dB, 27.25 dB to 35.75 dB, and
26.93 dB to 34.93 dB) for Brain, Spine, and Knee respectively. For
the subjective evaluation test, the results show that the compression
ratio of 40:1 was acceptable for brain image, whereas for spine and
knee images 50:1 was acceptable.
 James H., Thrall, Giles Boland, "Telemedicine in Practice", Seminars in
nuclear medicine, volume xxvii. No.2, April 1998, pp.145-157
 B. R. Sanders, J. H. Shanon, " Telemedicine: Theory and Practice",
Springfield, Illinois, 1997.
 Rafael C. Gonzalez, Richard E. Woods "Digital Image Processing" 2nd
edition, Pearson Prentice Hall, 2002
 Persons K., Pallison P., Patrice M., Manduca A., Willian J., Charboneau.
"Ultrasound grayscale image compression with JPEG and Wavelet
techniques ", Journal of Digital Imaging, 13: 25-32, 2000.
 Bradley J. and Erickson M.D, "Irreversible Compression of Medical
Images", Department of Radiology, Mayo Foundation, Journal of Digital
Imaging, vol.15, No.1, pp.5-14, 2002
 Sayre J., Aberle D., and Boechat I., "The Effect of Data Compression on
Diagnostic Accuracy in Digital Hand and Chest Radiography",
Proceedings of SPIE, 1653: 232-240, 1992.
 S.E.Ghrare, M.A.M.Ali, K.Jumari, M.Ismail, "The Effect of Image Data
Compression on the Clinical Information Quality of Compressed
Computed Tomography Images for Teleradiology Applications",
European Journal of Scientific Research, Vol.23 No.1 (2008), pp.6-12
 Rafael C. Gonzalez, Richard E. Woods and Steven L. Eddins "Digital
Image Processing using MATLAB", Prentice Hall, 2004.
 Ahmet M., Paul S., "Image quality measures and their performance",
IEEE Transactions on communications, 43:2959-2965, 1995.
 Lee H., Haynor D., and Kim Y. "Subjective evaluation of compressed
image quality" Proceedings of SPIE, Image Capture, Formatting and
Display, 1653: 241-245, 1992. S. Chen, B. Mulgrew, and P. M. Grant,
"A clustering technique for digital communications channel equalization
using radial basis function networks," IEEE Trans. Neural Networks,
vol. 4, pp. 570-578, July 1993.
 Pamela, C., Robert, M., Richard, A. "Evaluating quality of compressed
medical images SNR, Subjective Rating, and Diagnostic Accuracy",
Proceeding of the IEEE, 82: 919-932, 1994