Feature-Based Retinal Image Registration Using Bifurcation Structures

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July 15, 2011
Registering retinal images
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 (2nd derivative) matrix
Tubular, plate-like and spherical structures
Vascular tree extraction
Original images
Vessel enhancement
Thresholding
+
Skeletonization
+
Largest connected
components
7 
6
8
L2  9
5 1
L1
4
2
3
10 11
12
L3
From bifurcation point to
bifurcation structure…
“Feature-Based Retinal Image Registration Using
Bifurcation Structures”, Chen & Zhang, 2009
x  [ L1, L2 , L3 , 1, 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11, 12 ]
But…
• L’s are normalized to sum up to 1.
• The α triplets sum up to 360.
Therefore we can remove some redundancy.
x  [ L1 , L2 , 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
Matching candidates
Vessel registration
Results: retinal registration (I)
Results: retinal registration (II)
Results: bad news...
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