Multispectral code excited linear prediction coding and its application in magnetic resonance images

Jian Hong Hu, Yao Wang, Patrick T. Cahill

Research output: Contribution to journalArticle

Abstract

This paper reports a multispectral code excited linear prediction (MCELP) method for the compression of multispectral images. Different linear prediction models and adaptation schemes have been compared. The method that uses a 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 nonoverlapping three-dimensional (3-D) macroblocks. Each macroblock is further divided into several 3-D micro-blocks, and the best excitation signal for each microblock is determined through an analysis-by-synthesis procedure. The MFCELP method has been applied to multispectral magnetic resonance (MR) images. To satisfy the high quality requirement for medical images, the error between the original image set and the synthesized one is further specified using a vector quantizer. This method has been applied to images from 26 clinical MR neuro studies (20 slices/study, three spectral bands/slice, 256 x 256 pixels/band, 12 b/pixel). The MFCELP method provides a significant visual improvement over the discrete cosine transform (DCT) based Joint Photographers Expert Group (JPEG) method, the wavelet transform based embedded zero-tree wavelet (EZW) coding method, and the vector tree (VT) coding method, as well as the multispectral segmented autoregressive moving average (MSARMA) method we developed previously.

Original languageEnglish (US)
Pages (from-to)1555-1566
Number of pages12
JournalIEEE Transactions on Image Processing
Volume6
Issue number11
DOIs
StatePublished - 1997

Fingerprint

Linear Prediction
Magnetic Resonance Image
Magnetic resonance
Coding
Pixels
Discrete cosine transforms
Wavelet transforms
Multispectral Images
Slice
Pixel
Magnetic Resonance
Discrete Cosine Transform
Autoregressive Moving Average
Medical Image
Autoregressive Model
Prediction Model
Wavelet Transform
3D
Linear Model
Wavelets

Keywords

  • CELP coding
  • Medical image compression
  • Multispectral image compression

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Graphics and Computer-Aided Design
  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition

Cite this

Multispectral code excited linear prediction coding and its application in magnetic resonance images. / Hu, Jian Hong; Wang, Yao; Cahill, Patrick T.

In: IEEE Transactions on Image Processing, Vol. 6, No. 11, 1997, p. 1555-1566.

Research output: Contribution to journalArticle

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