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Medical Image Compression by Sampling DCT Coefficients

Explore image compression using Discrete Cosine Transform (DCT), Zigzag Scanning, Adaptive Sampling, and Huffman Coding in medical imagery. Achieve better results than traditional methods.

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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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