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Experimental Results on the Classification of UTE and McFlash Sequences

Experimental Results on the Classification of UTE and McFlash Sequences. Giovanni Motta Jan 21, 2005. Unupervised Classification. Voxels are divided into 16 classes with a K-means algorithm A class is assigned to each voxel, similar voxels belong to the same class

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Experimental Results on the Classification of UTE and McFlash Sequences

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  1. Experimental Results on the Classification of UTE and McFlash Sequences Giovanni Motta Jan 21, 2005

  2. Unupervised Classification • Voxels are divided into 16 classes with a K-means algorithm • A class is assigned to each voxel, similar voxels belong to the same class • Classification is visualized with maps where different colors represent different classes • At the present, color assignment is random; some color assignments look “better” (more contrasted) then other. Evaluating the results may be hard because of this

  3. Unupervised Classification • The classifier is trained on a ROI that is manually selected for each image • The ROI excludes the background • Results are reported for classification of: • Original voxel vectors V(i,j) • Mean removed voxels V(i,j)- mean(V(i,j)) • Unitary voxels V(i,j)/|V(i,j)| • Mean removed, unitary voxels (V(i,j)- mean(V(i,j))) / | V(i,j)- mean(V(i,j)) |

  4. Sequences • UTE • Fat saturation • 4 echoes • 20 sequences 256x256 (4) or 320x320 (16) • TE = 0.08, 3.25, 6.42 and 9.59ms (2) • TE = 0.08, 4.53, 8.98 and 13.5ms (11) • TE = 0.08, 5.81, 11.6 and 17.4ms (4) • TE = 0.08, 6.90, 13.8 and 19.6ms (3)

  5. UTE_0001 Original Mean Removed Unitary Mean + Unitary

  6. UTE_0002 Original Mean Removed Unitary Mean + Unitary

  7. UTE_0003 Original Mean Removed Unitary Mean + Unitary

  8. UTE_0004 Original Mean Removed Unitary Mean + Unitary

  9. UTE_0005 Original Mean Removed Unitary Mean + Unitary

  10. UTE_0006 Original Mean Removed Unitary Mean + Unitary

  11. UTE_0007 Original Mean Removed Unitary Mean + Unitary

  12. UTE_0008 Original Mean Removed Unitary Mean + Unitary

  13. UTE_0009 Original Mean Removed Unitary Mean + Unitary

  14. UTE_0010 Original Mean Removed Unitary Mean + Unitary

  15. UTE_0011 Original Mean Removed Unitary Mean + Unitary

  16. UTE_0012 Original Mean Removed Unitary Mean + Unitary

  17. UTE_0013 Original Mean Removed Unitary Mean + Unitary

  18. UTE_0014 Original Mean Removed Unitary Mean + Unitary

  19. UTE_0015 Original Mean Removed Unitary Mean + Unitary

  20. UTE_0016 Original Mean Removed Unitary Mean + Unitary

  21. UTE_0017 Original Mean Removed Unitary Mean + Unitary

  22. UTE_0018 Original Mean Removed Unitary Mean + Unitary

  23. UTE_0019 Original Mean Removed Unitary Mean + Unitary

  24. UTE_0020 Original Mean Removed Unitary Mean + Unitary

  25. Sequences • McFlash • Non fat saturated • 9 echoes • Classification on the original voxels and on the voxels after Mark’s SVD denoising

  26. McFlash (Noisy) Original Mean Removed Unitary Mean + Unitary

  27. McFlash (SVD Denoised) Original Mean Removed Unitary Mean + Unitary

  28. To Do • Find a criterion to assign a unique colormap so that results can be easily compared • Compare with classification based on parametric representation (Ma, Mb, etc..) • Train on specific ROI (fibrosis, HCC, normal liver)

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