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

J H Hu1, Y Wang, P T Cahill

  • 1Dept. of Electr. Eng., Polytech. Univ., Brooklyn, NY.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1997
PubMed
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A new multispectral code excited linear prediction (MCELP) method, MFCELP, offers improved compression for multispectral images. This advanced technique provides better performance, complexity, and robustness, especially for medical imaging applications.

Area of Science:

  • Medical Imaging
  • Image Compression
  • Signal Processing

Background:

  • Multispectral images require efficient compression techniques.
  • Existing methods like JPEG and EZW have limitations in preserving multispectral data quality.
  • Previous work includes the multispectral segmented autoregressive moving average (MSARMA) method.

Purpose of the Study:

  • To develop and evaluate a novel multispectral image compression method.
  • To compare different linear prediction models and adaptation schemes.
  • To optimize compression for multispectral magnetic resonance (MR) images.

Main Methods:

  • A forward adaptive autoregressive (AR) model was employed for multispectral code excited linear prediction (MCELP).
  • The proposed method, MFCELP, utilizes 3-D macroblocks and micro-blocks with analysis-by-synthesis for excitation signal determination.

Related Experiment Videos

  • Vector quantization was used to specify the error for high-quality medical image reconstruction.

    • Main Results:

    • The MFCELP method demonstrated a good balance of performance, complexity, and robustness.
    • Significant visual improvements were observed compared to JPEG, EZW, VT, and MSARMA methods.
    • The method was successfully applied to a large dataset of multispectral MR neuro images.

    Conclusions:

    • MFCELP is a highly effective method for compressing multispectral images, particularly MR images.
    • The approach offers superior visual quality and compression efficiency over existing techniques.
    • This method holds promise for enhancing medical image storage and transmission.