1 / 11

Advanced Retinal Image Registration and Vessel Enhancement Techniques

This document presents a comprehensive study on retinal image registration and vessel enhancement techniques, focusing on algorithms such as "Multiscale vessel enhancement filtering" and "Feature-Based Retinal Image Registration Using Bifurcation Structures." It details the methodologies for extracting key features from vascular structures, applying affine transformations for accurate registration, and utilizing local image descriptors. Central to the analysis is the exploration of bifurcation structures in retinal images, leading to effective matching and greater precision in vascular tree extraction.

aspen-ellison
Télécharger la présentation

Advanced Retinal Image Registration and Vessel Enhancement Techniques

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. Overview

  3. 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

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

  5. 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!

  6. 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.

  7. Results: feature extration All candidates Vessel registration Matching candidates

  8. Results: retinal registration (I)

  9. Results: retinal registration (II)

  10. Results: bad news...

  11. Questions?

More Related