The purpose of this research project was to improve the quantification of microvascular networks depicted in contrast-enhanced ultrasound (CEUS) images of human hepatocellular carcinoma (HCC). Due to limited anatomical information in CEUS images, grayscale B-mode ultrasound (US) data is preferred when estimating tissue motion. Transformation functions derived from the B-mode data are one solution for registering a dynamic sequence of CEUS images. Microvessel density (MVD) can then be calculated from both the original and motion corrected CEUS images as the ratio of the number of contrast-enhanced image pixels with a value greater than zero to the number of pixels of the entire tumor space. Using US images of HCC before and after treatment with transarterial chemoembolization, results revealed that affine and non-rigid motion correction improves visualization and quantitative analysis of clinical data. Using the correlation coefficient (CC) between CEUS frames as metric of tissue motion, our motion correction strategy produced a 20% increase in the average CC from motion corrected frames compared to the data before correction (p \lt 0.001). Furthermore, enhanced visualization of microvascular networks in the treated liver tumor space may improve determination of treatment efficacy and need for any repeat procedures.