Retinal image matching using hierarchical vascular features

Authors:
Alauddin Bhuiyan;Ecosse Lamoureux;Baikunth Nath;Kotagiri Ramamohanarao;Tien Y. Wong
Affiliations:
Department of Computer Science and Software Engineering, The University of Melbourne, Melbourne, VIC, Australia and Centre for Eye Research Australia, The University of Melbourne, Melbourne, VIC, ...;Centre for Eye Research Australia, The University of Melbourne, Melbourne, VIC, Australia;Department of Computer Science and Software Engineering, The University of Melbourne, Melbourne, VIC, Australia;Department of Computer Science and Software Engineering, The University of Melbourne, Melbourne, VIC, Australia;Centre for Eye Research Australia, The University of Melbourne, Melbourne, VIC, Australia
Venue:
Computational Intelligence and Neuroscience - Special issue on Selected Papers from the 4th International Conference on Bioinspired Systems and Cognitive Signal Processing
Year:
2011

Citing 7
Cited 0

The Art of Computer Programming, 2nd Ed. (Addison-Wesley Series in Computer Science and Information

The Art of Computer Programming, 2nd Ed. (Addison-Wesley Series in Computer Science and Information
Digital Image Processing (3rd Edition)

Digital Image Processing (3rd Edition)
Personal authentication using digital retinal images

Pattern Analysis & Applications
A Tree Matching Approach for the Temporal Registration of Retinal Images

ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Automatic Detection of Vascular Bifurcations and Crossovers from Color Retinal Fundus Images

SITIS '07 Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System
Vascular Tree Construction with Anatomical Realism for Retinal Images

BIBE '09 Proceedings of the 2009 Ninth IEEE International Conference on Bioinformatics and Bioengineering
Retinal fundus image registration via vascular structure graph matching

Journal of Biomedical Imaging

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a method for retinal image matching that can be used in image matching for person identification or patient longitudinal study. Vascular invariant features are extracted from the retinal image, and a feature vector is constructed for each of the vessel segments in the retinal blood vessels. The feature vectors are represented in a tree structure with maintaining the vessel segments actual hierarchical positions. Using these feature vectors, corresponding images are matched. The method identifies the same vessel in the corresponding images for comparing the desired feature(s). Initial results are encouraging and demonstrate that the proposed method is suitable for image matching and patient longitudinal study.