This paper describes two semiautomated methods of segmentation of breast tumors from dynamic MR images obtained subsequent to administration of gadopentate dimeglumine. The first method, based on temporal correlation, generates a similarity map from the dynamic scans in which the value of each pixel is determined by its temporal similarity to a reference region of interest. The second method uses multispectral analysis and generates a feature map from a scatterplot of pixel intensities in the pre- and postcontrast images. The segmentation methods were tested on malignant and benign breast lesions in 11 patients with a range of tumor volumes and percentage contrast enhancement. The accuracy of both segmentation techniques and reproducibility of the multispectral method were investigated. A comparison of the two methods established that the temporal correlation method was superior based on accuracy, extent of user interaction, and speed of segmentation.