Foot ulcers are a severe complication of diabetes mellitus. Assessment of the vascular status of diabetic foot ulcers with Laser Speckle Contrast Imaging (LSCI) is a promising approach for diagnosis and prognosis. However, manual assessment during analysis of LSCI limits clinical applicability. Our aim was to develop and validate a fast and robust tracking algorithm for semi-automatic analysis of LSCI data. The feet of 33 participants with diabetic foot ulcers were recorded with LSCI, including at baseline, during the Post-Occlusive Reactive Hyperemia (PORH) test, and during the Buerger’s test. Different regions of interest (ROIs) were used to measure microcirculation in different areas of the foot. A tracking algorithm was developed in MATLAB to reposition the ROIs in the LSCI scans. Manual- and algorithm-tracking of all recordings were compared by calculating the Intraclass Correlation Coefficient (ICC). The algorithm was faster in comparison with the manual approach (90 s vs. 15 min). Agreement between manual- and algorithm-tracking was good to excellent during baseline (ICC = 0.896–0.984; p < 0.001), the PORH test (ICC = 0.790–0.960; p < 0.001), and the Buerger’s test (ICC = 0.851–0.978; p < 0.001), resulting in a tracking algorithm that delivers assessment of LSCI in diabetic foot ulcers with results comparable to a labor-intensive manual approach, but with a 10-fold workload reduction.