This paper presents a new algorithm to compute skeletons of noisy images of objects which can be described as ``amorphous blobs.'' Such a requirement arose from our research to obtain a better understanding of the role of the pseudopod in leukocyte locomotion. It involves the modeling and detection of pseudopods which are by their nature nonrigid bodies appearing on the cell's surface membrane. By computing skeletons at different resolutions, a filtered version can be produced without violating the constraints imposed by the semantic knowledge of pseudopod morphology. The filtered version incorporates all the significant ``events'' that occur at the different resolutions. The resolution at which the shape is examined is related to the degree of smoothing, in that the lower the resolution gets, the higher the degree of smoothing. Skeleton branches that persist over several scales arise from convexities that are locally as well as globally significant. Their stability is related to their perceptual significance. Our approach is to combine an initial region centered description (skeleton) with a boundary analysis executed at different resolutions. Having computed the skeleton at different scales, we then use those computed at the lower resolutions as a measure of how global the underlying convexity is. Clearly the skeletons computed at higher resolutions represent the exact location and orientation of the underlying convexities.