This paper tests a new, fully automated image segmentation algorithm and compares its results with conventional threshold-based edge detection techniques. A CT phantom-based method is used to measure the precision and accuracy of the new algorithm in comparison to two edge detection variants. These algorithms offer a high degree of noise and differential lighting immunity and allow multi-channel image data, making them ideal candidates for multi-echo MRI sequences. The algorithm considered in this paper employs a fast numerical method for energy minimization of the free boundary problem that can incorporate regional image characteristics such as texture or other scale-specific features. It relies on a recursive region merge operation, thus providing a series of nested segmentations. In addition to the phantom testing, we discuss the results of this fast, multiscale, pyramidal segmentation algorithm applied to MRI images. The CT phantom segmentation is measured by the geometric fidelity of the extracted measurements to the geometry of the original bone components. The algorithm performed well in phantom experiments, demonstrating an average four-fold reduction in the error associated with estimating the radius of a small bone although the standard deviation of the estimate was almost twice that of the edge detection techniques. Modifications are proposed which further improve the geometric measurements. Finally, the results on soft-tissue discrimination are promising, and we are continuing to enhance the core formulation to improve the segmentation of complex shaped regions.