Angiograms Synthetic Images from Tridimensional Analytical Modelling

C. Wecker and J. Schmith
Additional contact information
C. Wecker: Unisinos University
J. Schmith: Unisinos University

Chapter Chapter 29 in Integral Methods in Science and Engineering, 2026, pp 441-455 from Springer

Abstract: Abstract With the rapid advance in computational power and the popularization of neural network libraries, numerous techniques have been proposed to aid in the diagnostic of diseases. However, neural networks need thousands of labeled images in a data set to allow its training. This becomes a greater challenge in the scope of angiograms, due to the low availability of public labeled data set. Hence this work proposes a tool for the generation of synthetic angiograms. The tool uses a three dimensional analytical model as a basis. The vessels over the surface were modeled by toroidal functions including branches, thinning, aneurysm and stenosis effects. Further an approach of x-ray beams trajectory analysis over the model was deployed. This method creates a new approach in formulating synthetic angiograms images. Since the basis model is analytical, it can be changed to fit any organ i.e. the heart. Here an example was shown to a simple approximation of an coronary angiogram that can be easily expanded to a retinal angiogram. The proposed tool was able to output images that closely resemble a coronary angiography as an application example, reproducing the effect of depth for the heart and the vessels from a tridimensional model to an image.

Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-04458-7_29

Ordering information: This item can be ordered from
http://www.springer.com/9783032044587

DOI: 10.1007/978-3-032-04458-7_29

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().