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Quality Evaluation of Compressed MRI Medical Images for Telemedicine Applications
Abstract: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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1077665Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2564
 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