Performance Analysis of Medical Image Compression

Abstract— The paper deals with evaluation of medical images by objective quality measures. Two main approaches to assess image quality are objective testing and subjective testing. Objective measures correlate well with the perceived image quality for the proposed compression algorithm. This paper presents an effective algorithm to compress and to reconstruct DICOM (Digital Imaging and COmmunications in Medicine) images. DICOM is a standard for handling, storing, printing and transmitting information in medical imaging. These medical images are volumetric consisting of a series of sequences of slices through a given part of the body. DICOM series of images are decomposed using Cohen-Daubechies-Feauveau (CDF) biorthoganal wavelet. The wavelet coefficients are encoded using Set Partitioning In Hierarchical Trees (SPIHT). Consistent quality images are generated by this method at a lower bit rate compared to JPEG and Fractal compression algorithms. The image quality is evaluated by various objective quality measures.

[1]  William A. Pearlman,et al.  A new, fast, and efficient image codec based on set partitioning in hierarchical trees , 1996, IEEE Trans. Circuits Syst. Video Technol..

[2]  Christoph Meinel,et al.  DICOM-image compression , 1999, Proceedings 12th IEEE Symposium on Computer-Based Medical Systems (Cat. No.99CB36365).

[3]  Peter Schelkens,et al.  Wavelet Coding of Volumetric Medical Datasets , 2003, IEEE Trans. Medical Imaging.

[4]  A. Robert Calderbank,et al.  Lossless image compression using integer to integer wavelet transforms , 1997, Proceedings of International Conference on Image Processing.

[5]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1991, CACM.

[6]  Jerome M. Shapiro,et al.  Embedded image coding using zerotrees of wavelet coefficients , 1993, IEEE Trans. Signal Process..

[7]  M. Mrak,et al.  Picture quality measures in image compression systems , 2003, The IEEE Region 8 EUROCON 2003. Computer as a Tool..

[8]  Y. Fisher Fractal image compression: theory and application , 1995 .

[9]  Michael F. Barnsley,et al.  Fractals everywhere , 1988 .