Interested in improving the speed and accuracy of vascular analysis? This paper proposes a novel image analysis method for evaluating residual strain in blood vessels, addressing limitations in traditional manual processes. The system speeds up image analysis and processing techniques, making arterial residual strain evaluations more efficient and reliable. Traditionally, photography, film development, large photograph printing, and digitization were involved when evaluating arterial residual strain, taking days to complete. The semi-automatic operation evaluates residual strain values and is based on a JAVA application designed, to ensure portability. The use of the image-based evaluation technique is validated by comparison of arterial residual strain evaluated using photographs and digitizing tablet. This JAVA-based application automates much of the process, enabling semi-automatic operation. The research highlights the significant advancements in accurately measuring arterial residual strain while drastically reducing time. It represents a significant improvement in evaluating vascular biomechanics and contributes to a better understanding of cardiovascular tissues.
Published in the International Journal on Artificial Intelligence Tools, this paper fits with the journal's focus on artificial intelligence and its applications. By presenting an image-based evaluation technique for vascular residual strain, the research contributes to the development of AI-driven tools for medical imaging and analysis, aligning with the journal's scope.