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Effect of Hybrid Data Compression Technique on the Diagnostic Accuracy of Region of Interest (ROI) on MRI Image of a Spine Disc Prolapse

Richard O. Oyeleke, Adetunji P. Adewole, Florence A. Oladeji Published in Image Processing

International Journal of Applied Information Systems
Year of Publication: 2014
© 2013 by IJAIS Journal
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  1. Richard O Oyeleke, Adetunji P Adewole and Florence A Oladeji. Article: Effect of Hybrid Data Compression Technique on the Diagnostic Accuracy of Region of Interest (ROI) on MRI Image of a Spine Disc Prolapse. International Journal of Applied Information Systems 7(5):16-20, July 2014. BibTeX

    	author = "Richard O. Oyeleke and Adetunji P. Adewole and Florence A. Oladeji",
    	title = "Article: Effect of Hybrid Data Compression Technique on the Diagnostic Accuracy of Region of Interest (ROI) on MRI Image of a Spine Disc Prolapse",
    	journal = "International Journal of Applied Information Systems",
    	year = 2014,
    	volume = 7,
    	number = 5,
    	pages = "16-20",
    	month = "July",
    	note = "Published by Foundation of Computer Science, New York, USA"


The cost of transmitting and archiving medical images are quite prohibitive due to their large sizes especially in digital radiology system such as: picture archiving and communication systems and teleradiology. In order to reduce storage requirements and improve transmission rate, there is need for compression. Usually, radiologists are only interested in the abnormal region of the image (known as the region of interest) in making diagnosis and interpretations; hence, this work investigates the effect of hybrid data compression technique on the diagnostic accuracy of region of interest (ROI) on magnetic resonance image (MRI) of a spine disc prolapsed. We extract the ROI from the original image and apply lossless Wavelet-Based Compression (WBC) on the ROI while the remainder image known as the non-region of interest is compressed using discrete cosine transform (DCT). A compression ratio of 7:1 was achieved. Finally, the diagnostic accuracy of the compressed ROI image was evaluated subjectively by a group of 30 evaluators comprising of 20 radiologists and 10 Radiographers. The results obtained show a 100% acceptance of the compressed ROI for healthy diagnosis and interpretation.


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Image compression, medical image, region of interest, image evaluation, diagnostic accuracy