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Arriving at an Optimum Value of Tolerance Factor for Compressing Medical Images

Authors: Sumathi Poobal, G. Ravindran

Abstract:

Medical imaging uses the advantage of digital technology in imaging and teleradiology. In teleradiology systems large amount of data is acquired, stored and transmitted. A major technology that may help to solve the problems associated with the massive data storage and data transfer capacity is data compression and decompression. There are many methods of image compression available. They are classified as lossless and lossy compression methods. In lossy compression method the decompressed image contains some distortion. Fractal image compression (FIC) is a lossy compression method. In fractal image compression an image is coded as a set of contractive transformations in a complete metric space. The set of contractive transformations is guaranteed to produce an approximation to the original image. In this paper FIC is achieved by PIFS using quadtree partitioning. PIFS is applied on different images like , Ultrasound, CT Scan, Angiogram, X-ray, Mammograms. In each modality approximately twenty images are considered and the average values of compression ratio and PSNR values are arrived. In this method of fractal encoding, the parameter, tolerance factor Tmax, is varied from 1 to 10, keeping the other standard parameters constant. For all modalities of images the compression ratio and Peak Signal to Noise Ratio (PSNR) are computed and studied. The quality of the decompressed image is arrived by PSNR values. From the results it is observed that the compression ratio increases with the tolerance factor and mammogram has the highest compression ratio. The quality of the image is not degraded upto an optimum value of tolerance factor, Tmax, equal to 8, because of the properties of fractal compression.

Keywords: Fractal image compression, IFS, PIFS, PSNR, Quadtree partitioning.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330357

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References:


[1] Arnaud E .Jacquin , "Image coding based on a fractal theory of iterated contractive image transformation", IEEE Transaction on Image Processing, Vol. 1, no.1, pp 18-30, Jan 1992.
[2] S.K.Mitra, C.A.Murthy and M.K.Kundu, "Technique for Fractal Image Compression using Genetic Algorithm", IEEE Transactions on Image Processing, Vol.7, no.4, Apr 1998, pp 583-593.
[3] S.K.Mitra, C.A.Murthy and M.K.Kundu, "Partitioned Iterated Function System: A new tool for digital imaging", IETE Journal of Research Vol.16, no.5, pp 279-298, Sep-Oct 2000 .
[4] D.Saupe and S. Jacob, "Variance based quadtrees in fractal image compression", Electronic Letters, Vol.33, no.1, pp 46-48, Jan1997.
[5] Y.Fisher, "Fractal Image Compression : Theory and Application" , Springer Verlag, New York, 1995.