The cross-sectional area of different fiber types is an important anatomic feature in studying the structure and function of healthy and diseased human skeletal muscles. However, such studies are hampered by the thousands of fibers involved when manual segmentation has to be used. We have developed a semiautomatic segmentation method that uses computational geometry and recent computer vision techniques to significantly reduce the time required to accurately segment the fibers in a sample. The segmentation is achieved by simply pointing to the approximate centroid of each fiber. The set of centroids is then used to automatically construct the Voronoi polygons, which correspond to individual fibers. Each Voronoi polygon represents the initial shape of one active contour model, called a snake. In the energy minimization process, which is executed in several stages, different external forces and problem-specific knowledge are used to guide the snakes to converge to fiber boundaries. Our results indicate that this approach for segmenting muscle fiber images is fast, accurate, and reproducible compared with manual segmentation performed by experts.