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An efficient lossless medical image transformation method by improving prediction model

Sepehrband, F ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/ICOSP.2010.5655764
  3. Abstract:
  4. Medical images include human body picture and it is used in diagnosis purpose [1]. Lossless compression of medical image is an application of medical imaging. During lossless compression task, transformation algorithm can be used to increase compression ratio. In Real time applications such as telemedicine and online diagnosis, hardware implementation accelerates the process. Hence, for such purposes medical compression is better to be simple. Lossless JPEG and JPEG2000 are some compression method. JPEG2000 gives better compression ratio. However, it is complex. In this paper an efficient method of lossless image transformation has been introduced by improving prediction model. Simulation results show that the new method has reduced the entropy compared to Differential Pulse Code Modulation (DPCM) and Discrete Wavelet Transform (DWT) which are used in JPEG and JPEG2000 respectively. Moreover, this method is cost effective due to computational complexity and entropy reduction
  5. Keywords:
  6. Image transformation ; JPEG standards ; Compression methods ; Compression ratios ; Efficient method ; Entropy reduction ; Hardware implementations ; Human bodies ; Image transformations ; IS costs ; JPEG 2000 ; Lossless ; Lossless compression ; Medical images ; On-line diagnosis ; Prediction model ; Real-time application ; Simulation result ; Transformation algorithm ; Compression ratio (machinery) ; Computational complexity ; Differential pulse code modulation ; Discrete wavelet transforms ; Entropy ; Hardware ; Image compression ; Mathematical models ; Signal processing ; Medical imaging
  7. Source: International Conference on Signal Processing Proceedings, ICSP, 24 October 2010 through 28 October 2010 ; 2010 , Pages 728-731 ; 9781424458981 (ISBN)
  8. URL: http://ieeexplore.ieee.org/document/5655764