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

Overview

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.

Results: feature extrationAll candidates

Matching candidates

Vessel registration

Results: retinal registration (I)

Results: retinal registration (II)

Results: bad news...

Questions?