University of Erlangen-Nuremberg

About: University of Erlangen-Nuremberg is a education organization based out in Erlangen, Bayern, Germany. It is known for research contribution in the topics: Population & Immune system. The organization has 42405 authors who have published 85600 publications receiving 2663922 citations.

Few‐Layer Antimonene by Liquid‐Phase Exfoliation

Abstract: We report on a fast and simple method to produce highly stable isopropanol/water (4:1) suspensions of few-layer antimonene by liquid-phase exfoliation of antimony crystals in a process that is assisted by sonication but does not require the addition of any surfactant. This straightforward method generates dispersions of few-layer antimonene suitable for on-surface isolation. Analysis by atomic force microscopy, scanning transmission electron microscopy, and electron energy loss spectroscopy confirmed the formation of high-quality few-layer antimonene nanosheets with large lateral dimensions. These nanolayers are extremely stable under ambient conditions. Their Raman signals are strongly thickness-dependent, which was rationalized by means of density functional theory calculations.

Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database

Abstract: Automatic assessment of retinal vessels plays an important role in the diagnosis of various eye, as well as systemic diseases. A public screening is highly desirable for prompt and effective treatment, since such diseases need to be diagnosed at an early stage. Automated and accurate segmentation of the retinal blood vessel tree is one of the challenging tasks in the computer-aided analysis of fundus images today. We improve the concept of matched filtering, and propose a novel and accurate method for segmenting retinal vessels. Our goal is to be able to segment blood vessels with varying vessel diameters in high-resolution colour fundus images. All recent authors compare their vessel segmentation results to each other using only low-resolution retinal image databases. Consequently, we provide a new publicly available high-resolution fundus image database of healthy and pathological retinas. Our performance evaluation shows that the proposed blood vessel segmentation approach is at least comparable with recent state-of-the-art methods. It outperforms most of them with an accuracy of 95% evaluated on the new database.