Registering retinal images

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Presentation transcript:

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 http://www.inf.u-szeged.hu/projectdirs/ssip2011/teamG

Vascular tree extraction Overview Vessel enhancement Vascular tree extraction Feature extraction Registration

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

Vascular tree extraction Original images Vessel enhancement Thresholding + Skeletonization Largest connected components

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!

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?