A Tree Matching Approach for the Temporal Registration of Retinal Images

Authors:
Xinyu Guo;Wynne Hsu;Mong Li Lee;Tien Yin Wong
Affiliations:
National University of Singapore, Singapore;National University of Singapore, Singapore;National University of Singapore, Singapore;National University of Singapore, Singapore
Venue:
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Year:
2006

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Retinal image matching using hierarchical vascular features

Computational Intelligence and Neuroscience - Special issue on Selected Papers from the 4th International Conference on Bioinspired Systems and Cognitive Signal Processing

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Abstract

The temporal registration of retinal images provides an important groundwork for doctors to monitor the progression of diseases. Retinal image registration is challenging because the intensity of the retina and the vascular structure can vary greatly over time. In this paper, we describe a tree matching approach to register retinal images. We model each vessel in a retinal image as a tree, called Vessel Feature Tree (VFT). We design a matching function to compute the similarity of a pair of vessels based on their VFTs. We develop a global alignment algorithm to compute the best match between the vessels in two images. Experiment results on 300 pairs of real-world retina images indicate that the proposed approach is able to achieve an accuracy of 93%.