Transcript Feature-Based Retinal Image Registration Using Bifurcation Structures
Registering retinal images
July 15, 2011 Babak Ghafaryasl Universitat Pompeu Fabra Csaba Molnár University of Szeged Antonio R. Porras Universitat Pompeu Fabra Arie Shaus Tel Aviv University
http://www.inf.u-szeged.hu/projectdirs/ssip2011/teamG
Overview Vessel enhancement Vascular tree extraction Feature extraction Registration
Vessel enhancement “
Multiscale vessel enhancement filtering
”, Frangi et al, 1998 - Scale Space representation - Local image descriptors - Eigenvalues of Hessain (2 nd derivative) matrix Tubular, plate-like and spherical structures
Vascular tree extraction Original images Vessel enhancement Thresholding + Skeletonization + Largest connected components
4 7 6 8
L 2
5 9 1 10 12 11
L 3
x
From
bifurcation point
to
bifurcation structure
… “
Feature-Based Retinal Image Registration Using Bifurcation Structures
”, Chen & Zhang, 2009 [ , 1 2 , 3 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ] 3 2
L 1
But… • L’s are normalized to sum up to 1.
• The α triplets sum up to 360.
Therefore we can remove some redundancy.
x
[ , 1 2 , 1 , 2 , 3 , 5 , 6 , 7 , 10 , 11 ]
We can measure a distance between such structures!
From
bifurcation structures
to
registration
…
Step 1
: Find bifurcation structures in both images.
Step 2
: Find the best match between two bifurcation structures. The match between 4 points (3 are enough) determines the affine transformation.
Step 3
: Find next best matches (taking the transformation into account); refine the affine transformation with more points.
Results: feature extration All candidates Vessel registration Matching candidates
Results: retinal registration (I)
Results: retinal registration (II)
Results: bad news...
Questions?