In this paper methods and their examination results for automatic segmentation and parameterization of vessels based on spectral domain optical coherence tomography (SD-OCT) of the retina are presented. We present three strategies for morphologic image processing of a fundus image reconstructed from OCT scans. A specificity of initial image processing for fundus reconstruction is analysed. Then, the parameterization step is performed based on the vessels segmented with the proposed algorithm. The influence of various methods on the vessel segmentation and fully automatic vessel measurement is analysed. Experiments were carried out with a set of 3D OCT scans obtained from 24 eyes (12 healthy volunteers) with the use of an Avanti RTvue OCT device. The results of automatic vessel segmentation were numerically compared with those prepared manually by the medical doctor experts.
Urbanization has a far-reaching impact on the environment, economy, political and social processes. Therefore, understanding the spatial distribution and evolution of human settlements is a key element in planning strategies that ensure the sustainable development of urban and rural settlements. Accordingly, it is very important to map human settlements and to monitor the development of cities and villages. Therefore, the problem of settlements has found its reflection in the creation of global databases of urban areas. Global settlement data have extraordinary value. These data allow us to carry out the quantitative and qualitative analyses as well as to compare the settlement network at a regional, national and global scale. However, the possibility of conducting both spatial and attribute analyses of these data would be even more valuable. The article describes how to prepare raster data so that they can be implemented into a vector database. It answers the questions whether it is possible to combine these data with databases available in Poland and what benefits it brings. It presents the methods of data generalization and the optimization of time and disk space. As a result of the study, two vector databases with GUF data were developed. The first database resolution is similar to the original (~12 m resolution) database, the second database contains less detailed (~20 m resolution) data, generalized using mathematical morphology. Both databases have been enriched with descriptive data obtained from the National Geodetic and Cartographic Resource.