Example application : Dave Gibson's medical image video ...

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Example application : Dave Gibson's medical image video ...

  1. 1. Video Compression for Medical Imaging by David Gibson
  2. 2. Contents <ul><li>Part 1: Compression Background </li></ul><ul><ul><li>Fundamentals of Compression </li></ul></ul><ul><ul><li>Video & Motion Compensation </li></ul></ul><ul><li>Part 2: Medical Imaging </li></ul><ul><ul><li>Example of the data + JPEG /Wavelet encoding </li></ul></ul><ul><ul><li>Motion compensation </li></ul></ul><ul><ul><li>Region of interest (ROI) coding </li></ul></ul>
  3. 3. Part 1 Video Compression Review
  4. 4. Foundations of Compression <ul><li>The Foundations of Compression involves looking at the data. </li></ul>
  5. 5. Foundations of Compression
  6. 6. Foundations of Compression DCT
  7. 7. Video Compression
  8. 8. Main Classification of Video Compression Methods <ul><li>Intra-frame methods </li></ul><ul><li>Uses single frames </li></ul><ul><li>e.g. MJPEG - JPEG applied to video </li></ul><ul><li>Inter-frame methods </li></ul><ul><li>Uses temporal information </li></ul><ul><li>e.g. MPEG-1/2, H.263 </li></ul><ul><li>Usual approach to video compression </li></ul>
  9. 9. Inter-frame methods <ul><li>Use Motion Compensation </li></ul>
  10. 10. Motion Compensation <ul><li>Exploitation of temporal redundancy. </li></ul>Frame 30 Frame 31 Motion Compensation
  11. 11. How Do We Motion Compensate? <ul><li>Compensate each pixel separately with its own motion vector? </li></ul><ul><li>Huge amount of motion data - More data than the original image! </li></ul><ul><li>Can’t afford to motion compensate each individual pixel. </li></ul>Error Data Motion Data
  12. 12. Solution <ul><li>One motion vector for a group of pixels. </li></ul><ul><li>Based on looking at the data. </li></ul>
  13. 13. Block Matching <ul><li>Foundation of most current video coders (MPEG 1/2, H.261/3). </li></ul>
  14. 14. Conclusions (part 1) <ul><li>Presented a brief summary of video compression methods </li></ul>
  15. 15. Part 2 Video Compression of Medical Images
  16. 16. Medical Imaging <ul><li>Angiogram Video; </li></ul><ul><ul><li>Pictures taken of the heart at 30 frames/second </li></ul></ul><ul><ul><li>512x512 images - 8 bits/pixel </li></ul></ul><ul><ul><li>Typical procedure - 5 minutes </li></ul></ul><ul><ul><ul><li>Resulting in 2.5GBytes of data per patient. </li></ul></ul></ul><ul><ul><ul><li>@64Kbits/sec - 80 hours. </li></ul></ul></ul><ul><ul><ul><li>@10Mb/sec - 30 minutes. </li></ul></ul></ul>
  17. 17. Summary <ul><li>Going to look at 3 aspects of the research we’ve been doing: </li></ul><ul><ul><li>Example of the data + JPEG/Wavelet encoding </li></ul></ul><ul><ul><li>Motion compensation </li></ul></ul><ul><ul><li>Region of interest (ROI) coding </li></ul></ul>
  18. 18. Example Angiogram Sequence
  19. 19. Example JPEG Coding
  20. 20. Still Frame Coding Methods : Wavelet <ul><li>Similar frequency approach to DCT. </li></ul><ul><li>But considered to give better results. </li></ul><ul><li>Operation on the whole image. </li></ul>
  21. 21. JPEG/Wavelet Comparison
  22. 22. Use an ‘off the shelf’ video coder? <ul><li>Typical results for an angiogram image @0.8bpp. </li></ul><ul><li>Comparison of intra- and inter-frame methods using DCT. </li></ul><ul><li>Motion compensation performs badly for this type of data. </li></ul><ul><li>Key Point: Compression effectiveness depends upon the data </li></ul>Single Frame 0 1 2 3 4 5 6 7 8 RMS Error Inter-frame Prediction
  23. 23. Motion Compensation - Failure? <ul><li>Conventional motion compensation assumptions : </li></ul><ul><ul><li>Distinct, opaque objects moving simply. </li></ul></ul><ul><li>Also, angiogram images contain high frequency uncorrelated texture. </li></ul>
  24. 24. Motion Compensation - Failure? <ul><li>Objects in angiograms are partially transparent. </li></ul><ul><li>Image is made up of several layers of bones and tissue, all moving differently. </li></ul><ul><li>Conventional motion compensation model doesn’t apply well. </li></ul>
  25. 25. Region of Interest (ROI) Coder <ul><li>Aim is to shift the allocation of bits from uninteresting areas of the image to more interesting ones. </li></ul><ul><li>Makes more efficient use of the available bits. </li></ul>
  26. 26. ROI Example : Simple Case <ul><li>Manual segmentation. </li></ul>ROI non-ROI
  27. 27. Example ROI coder <ul><li>Example of transferring bits from non-ROI to ROI </li></ul>
  28. 28. ROI : Simple Case - Results <ul><li>Much lower error in the ROI at the expense of the non-ROI. </li></ul>0 0.5 1 1.5 2 2.5 3 3.5 4 0 1 2 3 4 5 6 7 8 Rate (bits/pixel) Distortion (RMS error) RD Graph with ROI - DFD Data (Global MC - M.Black) No ROI (baseline comparison) ROI Distortion Non-ROI Distortion
  29. 29. Key Aim <ul><li>Reallocate bits from diagnostically unimportant areas into diagnostically interesting ones </li></ul>
  30. 30. Eye Tracking (proof of concept) <ul><li>Experiment to identify key areas of an angiogram image. </li></ul>
  31. 31. Example Results (Expert)
  32. 32. Example Results (Sandra)
  33. 33. Eye Tracking <ul><li>Significant areas of the image are not directly examined. </li></ul>
  34. 34. <ul><li>Methods of measuring image quality: </li></ul><ul><ul><li>Classical RMS - Measure of intensity level difference for each pixel. </li></ul></ul><ul><ul><li>Perceptual measure - Takes in to account the observer. </li></ul></ul>Quality Measure and Results
  35. 35. Quality Measure and Results <ul><li>Perceptual measurement of image quality. </li></ul>1 2 3 4 5 Poor Perfect Original Compressed
  36. 36. What’s next for video compression research? <ul><li>More efficient compression methods - to better take advantage of data (e.g. object based) </li></ul><ul><li>Perceptual coding - introducing the viewer into the equation </li></ul>
  37. 37. Questions?

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