Comparison of Compression Ability Using DCT and Fractal Technique on Different Imaging Modalities

Image compression is one of the most important applications Digital Image Processing. Advanced medical imaging requires storage of large quantities of digitized clinical data. Due to the constrained bandwidth and storage capacity, however, a medical image must be compressed before transmission and storage. There are two types of compression methods, lossless and lossy. In Lossless compression method the original image is retrieved without any distortion. In lossy compression method, the reconstructed images contain some distortion. Direct Cosine Transform (DCT) and Fractal Image Compression (FIC) are types of lossy compression methods. This work shows that lossy compression methods can be chosen for medical image compression without significant degradation of the image quality. In this work DCT and Fractal Compression using Partitioned Iterated Function Systems (PIFS) are applied on different modalities of images like CT Scan, Ultrasound, Angiogram, X-ray and mammogram. Approximately 20 images are considered in each modality and the average values of compression ratio and Peak Signal to Noise Ratio (PSNR) are computed and studied. The quality of the reconstructed image is arrived by the PSNR values. Based on the results it can be concluded that the DCT has higher PSNR values and FIC has higher compression ratio. Hence in medical image compression, DCT can be used wherever picture quality is preferred and FIC is used wherever compression of images for storage and transmission is the priority, without loosing picture quality diagnostically.

Authors:

• Sumathi Poobal
• G. Ravindran

Keywords:

• DCT
• FIC
• PIFS
• PSNR.

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, Jan 1992, pp18-30.
[2] Anil. K. Jain, Fundamentals of Digital Image Processing, PHI, New
Delhi, 1995.
[3] Y. Fisher, Fractal Image Compression: Theory and Application,
Springer Verlag, New York, 1995.
[4] Micheal. F. Barnsley "Fractal Image Compression", Notices of the
AMS, June 1996, pg. 657-662.
[5] A.N. Netravali and B.G. Haskell, Digital Pictures:
Representation, Compression, and Standards (2nd Ed), Plenum Press,
New York, NY (1995).
[6] B. B. Mandelbrot, Fractal Geometry of Nature, W. H. Freeman and Co,
New York, 1982.
[7] 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, Sep-Oct 2000, pp 279-298.
[8] Rafael Conzalez, Paul Wintz, Digital Image Processing,
Addison-Wesley Publishing Company, Inc., 1987.
[9] D. Saupe and S. Jacob, "Variance based Quad-trees in fractal Image
compression," Electronic Letters , Vol. 33, No.1, 1997, pp. 46-48.
[10] KMS Soyjauadha, I. Jammer Bacus "Fractal image compression",
International Journal of electrical Engineering Education, Jan.2002.
[11] http://www.cs.cf.ac.uk/Dave/Multimedia/node231.html