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Medical Image Compression by Sampling DCT Coefficients

Medical Image Compression by Sampling DCT Coefficients. Wu, Yung-Gi, IEEE Transactions on Information Technology in Biomedicine , vol. 6, no. 1, March 2002, pp. 86-94 Adviser : Dr. Chang, Chin-Chen Reporter : Chi, Kang-Liang Date : 2003/02/25. Outline. Introduction

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Medical Image Compression by Sampling DCT Coefficients

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  1. Medical Image Compression by Sampling DCT Coefficients Wu, Yung-Gi, IEEE Transactions on Information Technology in Biomedicine, vol. 6, no. 1, March 2002, pp. 86-94 Adviser : Dr. Chang, Chin-Chen Reporter : Chi, Kang-Liang Date : 2003/02/25

  2. Outline • Introduction • Discrete Cosine Transform ( DCT ) • Zigzag Scanning • Classification • Adaptive Sampling • Huffman Code • Simulation Results • Conclusions

  3. Introduction Divide into Sub-blocks DCT & Quan. Medical Image Zig-Zag scanning Classification Storage Or Transmission Huffman Encoding Adaptive Sampling Fig. 1. Encoder system configuration

  4. Discrete Cosine Transform ( DCT ) Formula : where if w=0 if w=1,2,…,n-1

  5. An example of DCT transformation 106 105 101 103 104 111 105 113 213 0 7 3 1 0 1 -1 106 105 101 103 104 111 105 113 -1 -4 -4 -2 0 -2 1 -2 106 105 101 103 104 111 105 113 1 3 2 1 0 0 0 1 106 105 101 103 104 111 105 113 -1 -2 -1 0 0 0 0 0 106 105 101 103 104 111 105 113 1 0 0 0 0 -1 0 0 111 101 97 104 102 105 112 111 0 0 0 0 0 0 0 0 129 108 105 98 102 103 109 105 0 0 0 0 0 0 0 0 140 122 102 97 103 104 111 108 0 0 0 0 0 0 0 0 = 2/64 * 6829 = 213.41 DC DCT transform

  6. Zigzag Scanning

  7. Classification Input : z(i) • Output : • Complicated class • if > (2) Pure class if <= where Fig. 2. X-ray image

  8. Adaptive Sampling • Purpose: • Process the AC coefficients of each DCT-transformed image block • Output • Number of significant points • Coordinates of each significant point

  9. Adaptive Sampling 8 6 4 2 0 -2 -4 -6

  10. Adaptive Sampling 8 6 4 2 0 -2 -4 -6

  11. Adaptive Sampling 8 Significant point & next initial point 6 4 2 0 -2 -4 -6

  12. Huffman Code • Goal: • Compress the significant points and the numbers of significant points of all image blocks • Output: • Huffman table (Huffman tree) • Encoded bit stream

  13. Huffman Code (an example) A 15 0 0 B 7 39 13 0 C 6 1 1 24 0 D 6 11 1 A 0 B 100 C 101 D 110 E 111 E 5 1

  14. Simulation Results PSNR (dB) Medical Images Bit Rate ( bpp ) : (Compression Ratio) Proposed method JPEG wavelets Angiogram 42.82 0.18 : ( 44 ) 38.49 42.12 X-ray 0.28 : ( 28 ) 35.95 34.67 34.51 Sonogram 0.42 : ( 19 ) 31.06 31.02 29.55 41.27 CT-Bone 0.25 : ( 32 ) 41.54 40.11

  15. Conclusions • The adaptive sampling algorithm gets better results than JPEG and wavelets in medical images • Idea (I) : Using the different threshold • Idea (II) : Compressing by sampling DWT coefficients

  16. Idea (I) 8 6 4 2 0 -2 -4 -6

  17. Idea (II) LL1 HL1 LH1 HH1

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