OBJECTIVE The goal of this study is to develop a computerized method that identifies a specific axillary lymph node (ALN) seen on ultrasound (US) with its most likely corresponding node on breast MRI (BMRI). This goal is an important step in developing a preoperative non-invasive method for staging breast cancer on the basis of multi-modality imaging. METHODS Twenty patients with newly diagnosed breast cancer were scanned on US and MRI. Two expert breast imaging radiologists independently correlated ALNs seen on US with BMRI, and this correlation was used as the gold standard. To correlate ALNs on US and BMRI, the cortex and hilum of each ALN was segmented using an ellipse fitting algorithm, then the ALN long and short axes and maximum cortical thickness (MCT) were computed. Three ALNs were chosen as candidates from the BMRI datasets for each lymph node seen on US. Finally, the Euclidean distances across all measurements between the US ALN and each of the three BMRI candidates were computed, and the smallest distance was reported as the correlation result. RESULTS Using the expert radiologists identified correlated BMRI slice as the ground truth, the shortest Euclidean distance successfully identified the same lymph node as the radiologists in 13 out of 16 ALNs (81.25%). In negative ALNs, the standard deviation for long and short axes was relatively large but that of maximum cortical thickness was small. Average maximum cortical thickness and its standard deviation measured in US were very close to those measured in MRI. There were no significant differences among the long axis, short axis, and MCT measurements between US and MRI-T2 weighted sequence (P > 0.05 paired t-test). CONCLUSIONS We performed a feasibility study which showed that computerized measurements of ALNs might be used to identify the same ALN on different modalities such as US and BMRI. This type of correlation would be valuable as it would allow the use of combined imaging parameters to be applied to the evaluation of ALNs in patients with breast cancer. It is hoped that the combined multi-modality information would provide a more robust non-invasive method of staging the axilla than is currently available.