The aim of this work is the development of the system that performs importing, archiving, processing and display of angiographic image data as well as conversion into 3D models. The developed system has been divided into eight stages. The implementation of the system has been performed involving the data from an animal study and 9 patients with angiography images of abdomen, upper and lower part of the legs. The development of the system was performed in the Python programming language and was planned to be integrated with other programming libraries that provide other functionalities within the DECODE project. The used artery detection algorithms are based on semi-automatic and automatic algorithms based on conventional image filters as well as supervised/unsupervised machine learning approaches. The aim of this research is the development of a system that can perform three-dimensional reconstruction of peripheral arteries based on two-dimensional X-ray angiography images. The determination of the spatial points of the peripheral arteries is performed based on the pixel distance from the edge of the blood vessel, assuming that the blood vessel is cylindrical in shape. This method provides fast and simple results in the form of a mesh of a three-dimensional object, while providing the possibility for use on smaller data sets that lack images from other angles or from other forms of medical imaging such as Optical Coherence Tomography and Intravascular Ultrasound. The results depict significant potential for 3D artery reconstruction with limited data.