Evaluation of Publicly Available Blood Vessel Segmentation Methods for Retinal Images
Retinal blood vessel structure is an important indicator of disorders related to diseases, which has motivated the development of various image segmentation methods for the blood vessels. In this study, two supervised and two unsupervised retinal blood vessel segmentation methods are quantitatively compared by using five publicly available databases with the ground truth for the vessels. The parameters of each method were optimized for each database with the motivation to achieve good segmentation performance for the comparison and study the importance of proper selection of parameter values. The results show that parameter optimization does not significantly improve the segmentation performance of the methods when the original data is used. However, the methods’ performance for new data differs significantly. Based on the comparison, Soares method as a supervised approach provided the highest overall accuracy and, thus, the best generalisability. Bankhead and Nguyen methods’ performance were close to each other: Bankhead performed better with ARIADB and STARE, whereas Nguyen was better with DRIVE. Sofka method is available only as an executable and its performance matched the others only with ARIADB.
How to Cite:
Vostatek, P. & Claridge, E. & Fält, P. & Hauta-Kasari, M. & Uusitalo, H. & Lensu, L., (2015) “Evaluation of Publicly Available Blood Vessel Segmentation Methods for Retinal Images”, Proceedings of the Ophthalmic Medical Image Analysis International Workshop 2(2015), 137-144. doi: https://doi.org/10.17077/omia.1037
Rights: Copyright © 2015 Pavel Vostatek, Ela Claridge, Pauli Fält, Markku Hauta-Kasari, Hannu Uusitalo, and Lasse Lensu