• DocumentCode
    1499779
  • Title

    Adaptive predictive multiplicative autoregressive model for medical image compression

  • Author

    Chen, Zuo-Dian ; Chang, Ruey-Feng ; Kuo, Wen-Jia

  • Author_Institution
    Lab. of Adv. Syst. Integration, Nat. Chung Cheng Univ., Chiayi, Taiwan
  • Volume
    18
  • Issue
    2
  • fYear
    1999
  • Firstpage
    181
  • Lastpage
    184
  • Abstract
    An adaptive predictive multiplicative autoregressive (APMAR) method is proposed for lossless medical image coding. The adaptive predictor is used for improving the prediction accuracy of encoded image blocks in our proposed method. Each block is first adaptively predicted by one of the seven predictors of the JPEG lossless mode and a local mean predictor. It is clear that the prediction accuracy of an adaptive predictor is better than that of a fixed predictor. Then the residual values are processed by the multiplicative autoregressive (MAR) model with Huffman coding. Comparisons with other methods [MAR, space-varying MAR (SMAR) and adaptive JPEG (AJPEG) models] on a series of test images show that our method is suitable for reversible medical image compression.
  • Keywords
    Huffman codes; adaptive signal processing; autoregressive processes; data compression; image coding; medical image processing; prediction theory; Huffman coding; JPEG lossless mode; adaptive JPEG model; adaptive predictive multiplicative autoregressive model; encoded image blocks; local mean predictor; lossless medical image coding; prediction accuracy; residual values; reversible medical image compression; space-varying multiplicative autoregressive model; Accuracy; Biomedical imaging; Computed tomography; Decorrelation; Entropy coding; Huffman coding; Image coding; Predictive models; Pulse modulation; Transform coding; Angiography; Brain; Echoencephalography; Humans; Image Processing, Computer-Assisted; Knee; Magnetic Resonance Imaging; Tomography, X-Ray Computed;
  • fLanguage
    English
  • Journal_Title
    Medical Imaging, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0278-0062
  • Type

    jour

  • DOI
    10.1109/42.759128
  • Filename
    759128