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Proceedings Paper

Code-excited linear predictive coding of multispectral MR images
Author(s): Jian-Hong Hu; Yao Wang; Patrick Cahill
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Paper Abstract

This paper reports a multispectral code excited linear predictive coding method for the compression of well-registered multispectral MR images. Different linear prediction models and the adaptation schemes have been compared. The method which uses forward adaptive autoregressive (AR) model has proven to achieve a good compromise between performance, complexity and robustness. This approach is referred to as the MFCELP method. Given a set of multispectral images, the linear predictive coefficients are updated over non-overlapping square macroblocks. Each macro-block is further divided into several micro-blocks and, the best excitation signals for each microblock are determined through an analysis-by-synthesis procedure. To satisfy the high quality requirement for medical images, the error between the original images and the synthesized ones are further specified using a vector quantizer. The MFCELP method has been applied to 26 sets of clinical MR neuro images (20 slices/set, 3 spectral bands/slice, 256 by 256 pixels/image, 12 bits/pixel). It provides a significant improvement over the discrete cosine transform (DCT) based JPEG method, a wavelet transform based embedded zero-tree wavelet (EZW) coding method, as well as the MSARMA method we developed before.

Paper Details

Date Published: 27 February 1996
PDF: 12 pages
Proc. SPIE 2727, Visual Communications and Image Processing '96, (27 February 1996); doi: 10.1117/12.233267
Show Author Affiliations
Jian-Hong Hu, Polytechnic Univ. (United States)
Yao Wang, Polytechnic Univ. (United States)
Patrick Cahill, Cornell Univ. Medical College (United States)


Published in SPIE Proceedings Vol. 2727:
Visual Communications and Image Processing '96
Rashid Ansari; Mark J. T. Smith, Editor(s)

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