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

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Presentation transcript:

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 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

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)) |

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)

UTE_0001 Original Mean Removed Unitary Mean + Unitary

UTE_0002 Original Mean Removed Unitary Mean + Unitary

UTE_0003 Original Mean Removed Unitary Mean + Unitary

UTE_0004 Original Mean Removed Unitary Mean + Unitary

UTE_0005 Original Mean Removed Unitary Mean + Unitary

UTE_0006 Original Mean Removed Unitary Mean + Unitary

UTE_0007 Original Mean Removed Unitary Mean + Unitary

UTE_0008 Original Mean Removed Unitary Mean + Unitary

UTE_0009 Original Mean Removed Unitary Mean + Unitary

UTE_0010 Original Mean Removed Unitary Mean + Unitary

UTE_0011 Original Mean Removed Unitary Mean + Unitary

UTE_0012 Original Mean Removed Unitary Mean + Unitary

UTE_0013 Original Mean Removed Unitary Mean + Unitary

UTE_0014 Original Mean Removed Unitary Mean + Unitary

UTE_0015 Original Mean Removed Unitary Mean + Unitary

UTE_0016 Original Mean Removed Unitary Mean + Unitary

UTE_0017 Original Mean Removed Unitary Mean + Unitary

UTE_0018 Original Mean Removed Unitary Mean + Unitary

UTE_0019 Original Mean Removed Unitary Mean + Unitary

UTE_0020 Original Mean Removed Unitary Mean + Unitary

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

McFlash (Noisy) Unitary Mean + Unitary Original Mean Removed

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

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