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HVS scheme for DICOM image compression: Design and comparative performance evaluation.

B Prabhakar1, M Ramasubba Reddy

  • 1Biomedical and Engineering Division, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India. prabhakarb@iitm.ac.in

European Journal of Radiology
|February 28, 2007
PubMed
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This study introduces an improved DICOM image compression system using Human Visual System (HVS) sensitivities and SPIHT quantization. The new method enhances visual quality and compression efficiency for medical imaging, outperforming existing models.

Area of Science:

  • Medical Imaging
  • Digital Signal Processing
  • Computer Vision

Background:

  • Digital imaging in medicine requires efficient DICOM compression for transmission and archival.
  • Existing compression methods may not fully leverage Human Visual System (HVS) characteristics.

Purpose of the Study:

  • To develop and evaluate a novel DICOM image compression system incorporating HVS sensitivities.
  • To compare the performance of different HVS models and wavelet filters for medical image compression.

Main Methods:

  • Wavelet transform and SPIHT quantization integrated with HVS models (Mannos et al., Daly).
  • Luminance Contrast Sensitivity Function (CSF) used for weighting wavelet subband coefficients.
  • Performance evaluated using Eskicioglu chart metric on various medical and natural images.

Related Experiment Videos

Main Results:

  • The proposed HVS-coded SPIHT compression system improved visual quality and Eskicioglu chart metric at equivalent compression ratios.
  • The Daly HVS model demonstrated superior perceptual and quantitative performance compared to the Mannos et al. model.
  • The "bior4.4" wavelet filter yielded better results than the "db9" filter.

Conclusions:

  • The developed technique offers competitive visual quality, compression ratio, and processing time compared to JPEG2000 (Kakadu).
  • HVS-based compression significantly enhances the efficiency and perceptual quality of DICOM images.
  • The Daly HVS model and "bior4.4" wavelet filter are recommended for advanced medical image compression.