Because of the large number of SAR images the Air Force generates and the dwindling number of available human analysts, automated methods must be developed. A key step towards automated SAR image analysis is image segmentation. There are many segmentation algorithms, but they have not been tested on a common set of images, and there are no standard test methods. This paper evaluates four SAR image segmentation algorithms by running them on a common set of data and objectively comparing them to each other and to human segmentations. This objective comparison uses a multi-measure approach with a set of master segmentations as ground truth. The measure results are compared to a Human Threshold, which defines the performance of human segmentors compared to the master segmentations. Also, methods that use the multi-measures to determine the best algorithm are developed. These methods show that of the four algorithms, Statistical Curve Evolution produces the best segmentations; however, none of the algorithms are superior to human segmentations. Thus, with the Human Threshold and Statistical Curve Evolution as benchmarks, this paper establishes a new and practical framework for testing SAR image segmentation algorithms.