Medical Image Compression

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B.Tech Final Year Presentation on Medical Image Compression (Later Modified).

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Medical Image Compression

  1. 1. Medical Image CompressionDigital Signal Processing Digital Image Processing Image Compression Medical Image Compression
  2. 2. Algorithms UsedJoint Photographic Experts Group DCTRegion Of InterestEmbedded Zerotree Wavelet DWTUnit Embedded Zerotree Wavelet
  3. 3. 1 Joint Photographic Experts Group o o Introduction Discrete Cosine Transformation o Algorithm o Compression Results
  4. 4. Joint Photographic Experts Group Introduction Bitmap Image, JPEG Compressed, 150KB 14KB Based on discrete cosine transformation Lossy compression method Mostly used by digital cameras and web usage
  5. 5. Joint Photographic Experts Group Discrete Cosine TransformationTime Domain Frequency Domain DCT is a time to frequency domain transformation.
  6. 6. Joint Photographic Experts Group Algorithm8x8 Zig-zag Huffmanpixel DCT Quantization RLE scan Encodingblocks Quantization Output Table Quantization results in loss of information . Compressed output is losslessly stored .
  7. 7. Joint Photographic Experts Group Compression Results MSE 250 200 150 Lena 100 X-Ray 50 MRI 0 0 0.5 1 1.5X-Ray MRI Bitrate PSNR 45 40 35 30 25 20 Lena 15 X-Ray 10 Lena 5 MRI 0 0 0.5 1 1.5 Bitrate
  8. 8. 2 Region Of Interest o Introduction o Compression Results o Comparison with JPEG
  9. 9. Region Of Interest IntroductionOriginal Image ROI Portion of image containing the significant information is selected as ROI and compressed at a higher quality .
  10. 10. Region Of Interest Compression Results MSE 140 120 100 80 60 X-ray 40 MRI 20 CAT Scan 0X-Ray MRI 0 0.2 0.4 0.6 0.8 Bitrate PSNR 50 40 30 20 X-ray 10 MRI CAT Scan 0 CAT Scan 0 0.2 0.4 0.6 0.8 Bitrate
  11. 11. Region Of Interest Comparison with JPEG 140 250 120 120 100 200 100 80MSE 80 150 60 60 100 40 40 50 20 20 0 0 0 0 0.5 1 0 0.5 1 1.5 0 0.5 1 50 50 40PSNR 40 40 30 30 30 20 20 20 10 10 10 0 0 0 0 0.5 1 0 0.5 1 0 0.5 1 1.5 X-Ray MRI CAT Scan ROI JPEG
  12. 12. 3 Embedded Zerotree Wavelet o o Introduction Discrete Wavelet Transformation o Zerotree Concept o An Example
  13. 13. Embedded Zerotree Wavelet IntroductionEmbedded – The EZW encoder is based onprogressive encoding. Progressive encoding is alsoknown as embedded encoding.Zerotree – A data structure called zero-tree is used inEZW algorithm to encode the data.Wavelet – The EZW encoder is specially designed towork with wavelet transform. It was originally designedto operate on images.
  14. 14. Embedded Zerotree Wavelet Discrete Wavelet TransformationOriginal First Second ThirdImage Level Level Level Lower sub-band has higher resolution and contains higher frequency information.
  15. 15. Embedded Zerotree Wavelet Zerotree Concept Quad-tree An Structure ExampleA zerotree is a quad-tree having all its descendents less than the current threshold.
  16. 16. Embedded Zerotree Wavelet An Example63 -34 49 10 7 13 -12 7P N P T Z Z Dominant Pass 1-31 23 14 -13 3 4 6 -1 Z T T T Z Z  Threshold = 3215 14 3 -12 5 -7 3 9  Output = T Z PNZTPTTTTZTTZZZZZ-9 -7 -14 8 4 -2 3 2 PZZ T T-5 9 -1 47 4 6 -2 2 Subordinate Pass 1 Z P3 0 -3 2 3 -2 0 4  List = {63 34 49 47 } Z Z  Output = 1 0 1 02 -3 6 -4 3 6 3 65 11 5 6 0 3 -4 4
  17. 17. 4 Unit Embedded Zerotree Wavelet * o o Drawback of existing algorithm Concept of Unit Cell o Formation of Unit Cell o Comparison with existing algorithm * Paper under Review process
  18. 18. Unit Embedded Zerotree Wavelet * Drawback of existing algorithm Dimensions Children Descendents 8x8 284 378 32 x 32 6,859 8,564 128 x 128 1,10,576 1,36,407 256 x 256 4,43,492 5,67,959Existing algorithm needs to check a large number of children and descendents. * Paper under Review process
  19. 19. Unit Embedded Zerotree Wavelet * Concept of Unit Cell 2 n 2 nSmallest possible square matrix generated from the wavelet decomposed image, having the same level of wavelet decomposition structure as the original image. * Paper under Review process
  20. 20. Unit Embedded Zerotree Wavelet * Formation of Unit Cell Decomposition Unit Cell Level Order 1 2 2 4 3 8 n 2nSmallest possible square matrix generated from the wavelet decomposed image, having the same level of wavelet decomposition structure as the original image. * Paper under Review process
  21. 21. Unit Embedded Zerotree Wavelet * Comparison to Existing Algorithm Original algorithm Proposed algorithm Image 32 64 128 256 32 64 128 256 × × × × × × × × 32 64 128 256 32 64 128 256 LENA 1.092 4.540 20.062 210.617 0.952 3.479 13.120 52.073BARBARA 1.108 4.477 19.407 213.425 0.983 3.572 13.915 54.632CAMRAMAN 1.139 4.618 20.639 200.960 0.998 3.588 13.463 53.867GOLDHILL 1.136 4.680 20.779 216.670 0.996 3.510 13.541 54.226 PEPPERS 1.186 4.524 19.812 214.626 1.030 3.650 14.258 54.632 Table showing coding time (seconds) for original and proposed algorithm. * Paper under Review process
  22. 22. Unit Embedded Zerotree Wavelet * Comparison to Existing Algorithm Percent Image 32 64 128 256 × × × × 80 32 64 128 256 70 60LENA 12.821 23.370 34.603 75.276 50 Percent 40BARBARA 11.282 20.214 28.299 74.402 30 20 10CAMRAMAN 12.379 22.304 34.769 73.195 0 32 × 32 64 × 64 128 × 128 256 × 256GOLDHILL 12.324 25.000 34.833 74.973 Image Dimensions (pixels) LENA BARBARA CAMRAMAN GOLDHILL PEPPERSPEPPERS 13.153 19.319 28.034 74.545 Table showing percentage improvement in coding time using proposed algorithm over original algorithm. * Paper under Review process

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