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Registering retinal images
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
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Vascular tree extraction
Overview Vessel enhancement Vascular tree extraction Feature extraction Registration
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Vessel enhancement - Scale Space representation
“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
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Vascular tree extraction
Original images Vessel enhancement Thresholding + Skeletonization Largest connected components
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From bifurcation point to bifurcation structure…
“Feature-Based Retinal Image Registration Using Bifurcation Structures”, Chen & Zhang, 2009 L2 L3 But… L’s are normalized to sum up to 1. The α triplets sum up to 360. Therefore we can remove some redundancy. L1 We can measure a distance between such structures!
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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.
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Results: feature extration
All candidates Vessel registration Matching candidates
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Results: retinal registration (I)
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Results: retinal registration (II)
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Results: bad news...
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Questions?
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