title: HVS-based medical image compression.
abstract: BACKGROUND
  With the promotion and application of digital imaging technology in the medical domain, the amount of medical images has grown rapidly. However, the commonly used compression methods cannot acquire satisfying results.
  
  
  METHODS
  In this paper, according to the existed and stated experiments and conclusions, the lifting step approach is used for wavelet decomposition. The physical and anatomic structure of human vision is combined and the contrast sensitivity function (CSF) is introduced as the main research issue in human vision system (HVS), and then the main designing points of HVS model are presented. On the basis of multi-resolution analyses of wavelet transform, the paper applies HVS including the CSF characteristics to the inner correlation-removed transform and quantization in image and proposes a new HVS-based medical image compression model.
  
  
  RESULTS
  The experiments are done on the medical images including computed tomography (CT) and magnetic resonance imaging (MRI). At the same bit rate, the performance of SPIHT, with respect to the PSNR metric, is significantly higher than that of our algorithm. But the visual quality of the SPIHT-compressed image is roughly the same as that of the image compressed with our approach. Our algorithm obtains the same visual quality at lower bit rates and the coding/decoding time is less than that of SPIHT.
  
  
  CONCLUSIONS
  The results show that under common objective conditions, our compression algorithm can achieve better subjective visual quality, and performs better than that of SPIHT in the aspects of compression ratios and coding/decoding time.
authors.affiliation: Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, 200030 Shanghai, China.
authors.forename: Xie
authors.initials: X
authors.lastname: Kai
authors.forename: Yang
authors.initials: Y
authors.lastname: Jie
authors.forename: Zhu Yue
authors.initials: ZY
authors.lastname: Min
authors.forename: Li Xiao
authors.initials: LX
authors.lastname: Liang
mesh_terms: D000465:Algorithms; D044962:Data Compression / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging; D015588:Observer Variation; D011601:Psychophysics; D014057:Tomography, X-Ray Computed; D014796:Visual Perception