Segmentation strategies for breast tumors from dynamic MR images.

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.