Lossless encoding of medical images: Hybrid modification of statistical modeling-based conception

Methods of lossless compression of medical image data are considered. Selected class of efficient algorithms have been constructed, examined, and optimized to conclude the most useful tools for medical image archiving and transmission. Image data scanning, 2D context-based prediction and interpolation, and statis- tical models of entropy coder have been optimized to compress ef- fectively ultrasound (US), magnetic resonance (MR), and computed tomography (CT) images. The SSM technique of suitable data de- composing scanning method followed by probabilistic modeling of the context in arithmetic encoding have occurred the most useful in our experiments. Context order, shape, and alphabet have been fitted to local data characteristics to decrease image data correlation and dilution of statistical model. Average bit rate value over test images is equal to 2.53 bpp for SSM coder and significantly over- comes 2.92 bpp of CALIC bit rate. Moreover, optimization of loss- less wavelet coder by thinking of efficient subband decomposition schemes, and integer-to-integer transforms is reported. Efficient hy- brid coding method (SHEC) as a complete tool for medical image archiving and transmission is proposed. SHEC develops SSM by including CALIC-like coder to compress the highest quality images and JPEG2000 wavelet coder for progressive delivering of high and middle quality images in telemedicine systems. © 2001 SPIE and IS&T. (DOI: 10.1117/1.1407261)

[1]  H. K. Huang,et al.  Radiologic image compression-a review , 1995, Proc. IEEE.

[2]  B. Widrow,et al.  Stationary and nonstationary learning characteristics of the LMS adaptive filter , 1976, Proceedings of the IEEE.

[3]  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..

[4]  Xiaolin Wu,et al.  Lossless compression of continuous-tone images via context selection, quantization, and modeling , 1997, IEEE Trans. Image Process..

[5]  Max A. Viergever,et al.  Reversible image data compression based on HINT (hierarchical interpolation) decorrelation and arithmetic coding , 1991, Medical Imaging.

[6]  Giovanni Motta,et al.  Adaptive linear prediction lossless image coding , 1999, Proceedings DCC'99 Data Compression Conference (Cat. No. PR00096).

[7]  John A. Robinson,et al.  Efficient general-purpose image compression with binary tree predictive coding , 1997, IEEE Trans. Image Process..

[8]  V. J. Mathews,et al.  Perceptually lossless image compression , 1997, Proceedings DCC '97. Data Compression Conference.

[9]  I. Daubechies,et al.  Wavelet Transforms That Map Integers to Integers , 1998 .

[10]  Artur Przelaskowski.  Performance evaluation of JPEG 2000-like data decomposition schemes in wavelet codec , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[11]  Abraham Lempel,et al.  Compression of two-dimensional data , 1986, IEEE Trans. Inf. Theory.

[12]  William H. Press,et al.  Numerical recipes in C. The art of scientific computing , 1987 .

[13]  Nasir D. Memon,et al.  An analysis of some common scanning techniques for lossless image coding , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[14]  Bernd Meyer,et al.  TMW - a new method for lossless image compression , 1997 .

[15]  Faouzi Kossentini,et al.  Reversible integer-to-integer wavelet transforms for image compression: performance evaluation and analysis , 2000, IEEE Trans. Image Process..

[16]  Xiaolin Wu.  High-order context modeling and embedded conditional entropy coding of wavelet coefficients for image compression , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[17]  David S. Taubman,et al.  High performance scalable image compression with EBCOT. , 2000, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society.

[18]  Guillermo Sapiro,et al.  The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS , 2000, IEEE Trans. Image Process..

[19]  W. Sweldens.  The Lifting Scheme: A Custom - Design Construction of Biorthogonal Wavelets "Industrial Mathematics , 1996 .

[20]  M. Loew,et al.  A methodology for modeling the distributions of medical images and their stochastic properties. , 1990, IEEE transactions on medical imaging.

[21]  Michael W. Marcellin,et al.  An overview of JPEG-2000 , 2000, Proceedings DCC 2000. Data Compression Conference.