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Efficient Hardware Implementation of Lossless Medical Image Compression

Sepehrband, Farshid | 2010

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 41051 (55)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Mortazavi, Mohammad; Ghorshi, Mohammad Ali
  7. Abstract:
  8. Medical images contain human body pictures and are widely used for diag-nosis and surgical purposes. Compression is needed for medical images for some applications such as profiling a patient's data or transmission systems. Due to the importance of the information of medical images, lossless or visually lossless compression is preferred. Lossless compression mainly consists of transformation and encoding steps. On the other hand, hardware implementation of lossless compression algorithm accelerates real time tasks such as online diagnosis and telemedicine. Lossless JPEG, JPEG-LS and lossless version of JPEG2000 are few well known methods for lossless compression. In this thesis, we introduce a new transformation method which is effcient for using in lossless compression of medical images due to its redundancy reduction and computational complexity. A new method is achieved by improving the perdition model of Differential Pulse Code Modulation (DPCM). Our new transformation increases the energy compaction of the prediction model and as a result the transformed image has a smaller entropy value. The proposed transformation method can be lossy, near-lossless, visually lossless and lossless. After proving the effciency of the new transformation method, a lossless compression method for medical images is then introduced by adding an optimize Huffman entropy encoder to the new transformation method. The proposed compression method is low complex and is suitable for hardware implementation. Further, our method is applied to more than hundreds of non-medical and medical test-cases and the results are compared with previous methods. The comparison is based on compression ability and algorithm simplicity so it can be suited for hardware implementation. As a result, the new algorithm shows better effciency for lossless compression of medical images, especially for real time applications
  9. Keywords:
  10. Lossless Compression ; Entropy ; Hardware Implementation ; Entropy Coding ; Image Transformation

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