A novel image processing strategy is detailed for simultaneous measurement of tumor perfusion and neovascular morphology parameters from a sequence of dynamic contrast-enhanced ultrasound (DCE-US) images. After normalization and tumor segmentation, a global time-intensity curve describing contrast agent flow was analyzed to derive surrogate measures of tumor perfusion (. i.e., peak intensity, time-to-peak intensity, area under the curve, wash-in rate, wash-out rate). A maximum intensity image was generated from these same segmented image sequences, and each vascular component was skeletonized via a thinning algorithm. This skeletonized data set and collection of vessel segments were then investigated to extract parameters related to the neovascular network and physical architecture (. i.e., vessel-to-tissue ratio, number of bifurcations, vessel count, average vessel length and tortuosity). An efficient computation of local perfusion parameters was also introduced and operated by averaging time-intensity curve data over each individual neovascular segment. Each skeletonized neovascular segment was then color-coded by these local measures to produce a parametric map detailing spatial properties of tumor perfusion. Longitudinal DCE-US image data sets were collected in six patients diagnosed with invasive breast cancer using a Philips iU22 ultrasound system equipped with a L9-3 transducer and Definity contrast agent. Patients were imaged using US before and after contrast agent dosing at baseline and again at weeks 6, 12, 18 and 24 after treatment started. Preliminary clinical results suggested that breast tumor response to neoadjuvant chemotherapy may be associated with temporal and spatial changes in DCE-US-derived parametric measures of tumor perfusion. Moreover, changes in neovascular morphology parametric measures may also help identify any breast tumor response (or lack thereof) to systemic treatment. Breast cancer management from early detection to therapeutic monitoring is currently undergoing profound changes. Novel imaging techniques that are sensitive to the unique biological conditions of each individual tumor represent valuable tools in the pursuit of personalized medicine.