  Recent advancements in AI have significantly transformed medical imaging,
particularly in angiography, by enhancing diagnostic precision and patient
care. However existing works are limited in analyzing the aorta and iliac
arteries, above all for vascular anomaly detection and characterization. To
close this gap, we propose Dr-SAM, a comprehensive multi-stage framework for
vessel segmentation, diameter estimation, and anomaly analysis aiming to
examine the peripheral vessels through angiography images. For segmentation we
introduce a customized positive/negative point selection mechanism applied on
top of the Segment Anything Model (SAM), specifically for medical (Angiography)
images. Then we propose a morphological approach to determine the vessel
diameters followed by our histogram-driven anomaly detection approach.
Moreover, we introduce a new benchmark dataset for the comprehensive analysis
of peripheral vessel angiography images which we hope can boost the upcoming
research in this direction leading to enhanced diagnostic precision and
ultimately better health outcomes for individuals facing vascular issues.
