Attila Budai

TL;DR: A method to reduce calculation time, achieve high accuracy, and increase sensitivity compared to the original Frangi method is presented and a new high resolution fundus database is proposed to compare it to the state-of-the-art algorithms.

TL;DR: The concept of matched filtering is improved, and the proposed blood vessel segmentation approach is at least comparable with recent state-of-the-art methods, and outperforms most of them with an accuracy of 95% evaluated on the new database.

TL;DR: The quantitative results consistently show that reproducibility is improved considerably by using the advanced algorithm, which also significantly outperforms the basic algorithm, and demonstrates that the advanced motion correction algorithm has the potential to improve the reliability of quantitative measurements derived from 3D-OCT data substantially.

TL;DR: A no-reference quality metric to quantify image noise and blur and its application to fundus image quality assessment is presented, which correlates reasonable to a human observer, indicating high agreement to human visual perception.

TL;DR: This work presents a multiscale algorithm for automatic retinal blood vessel segmentation, which is considered as a requirement for the diagnosis of vascular diseases, and uses a Gaussian resolution hierarchy to decrease computational needs.