Registering retinal images

July 15, 2011 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 (2 nd derivative) matrix Tubular, plate-like and spherical structures

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

 4  7  6  8

L 2

 5  9  1  10   12 11

L 3

x

 From

bifurcation point

to

bifurcation structure

… “

Feature-Based Retinal Image Registration Using Bifurcation Structures

”, Chen & Zhang, 2009 [ , 1 2 , 3 ,             1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]   3 2

L 1

But… • L’s are normalized to sum up to 1.

• The α triplets sum up to 360.

Therefore we can remove some redundancy.

x

 [ , 1 2 ,         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 Vessel registration Matching candidates

Results: retinal registration (I)

Results: retinal registration (II)

Results: bad news...

Questions?