Due to its portability and lack of ionizing radiation, three-dimensional ultrasound imaging is emerging as an important diagnostic tool in medicine. However, ultrasound images are usually noisy because of scattering and other complicated interactions between ultrasonic pulses and human tissue. This makes it difficult to automatically segment the images. Methods such as edge finding and region growing, which are primarily driven by the image data, can be easily diverted by speckle and broken contours. We have developed a new, model-driven segmentation method that automatically finds closed contours in noisy images using a global search. In particular, we have concentrated our research on horizontal image slices of the lower leg. The segmentation for such an image consists of three contours: the outer skin surface and two bones. Using a genetic algorithm, our method searches through the space of all possible contours, where contours are modeled as closed cubic splines. Candidate contours are blurred with a point spread function that approximates the point spread function of the image. These model contours are then compared to the actual image using correlation. We demonstrate the method on ultrasound images of human legs. Because this new algorithm avoids the diversions of noise, it works well in spite of the vagaries of ultrasound images, and it does so with a minimum of tunable parameters.