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Registering retinal images
Babak Ghafaryasl Universitat Pompeu Fabra
Csaba Molnár University of Szeged
Antonio R. PorrasUniversitat Pompeu Fabra
Arie Shaus Tel Aviv University
July 15, 2011
http://www.inf.u-szeged.hu/projectdirs/ssip2011/teamG
Vessel enhancement
- Scale Space representation - Local image descriptors- Eigenvalues of Hessain (2nd derivative) matrix
Tubular, plate-like and spherical structures
“Multiscale vessel enhancement filtering”, Frangi et al, 1998
Vascular tree extraction
Original images
Vessel enhancement
Thresholding +
Skeletonization+
Largest connected components
11106
12
1
234
5
78
9 L3
L1
L2
From bifurcation point to bifurcation structure…
1 2 3 1 2 3 4 5 6 7 8 9 10 11 12[ , , , , , , , , , , , , , , ]x L L L
But…• L’s are normalized to sum up to 1.• The α triplets sum up to 360.Therefore we can remove some redundancy.
1 2 1 2 3 5 6 7 10 11[ , , , , , , , , , ]x L L
“Feature-Based Retinal Image Registration Using Bifurcation Structures”, Chen & Zhang, 2009
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.