We describe detectors capable of locating small tumors of variable size in the highly textured anatomic backgrounds typical of gamma-ray images. The problem of inhomogeneous background noise is solved using a spatially adaptive statistical scaling operation, which effectively pre-whitens the data and leads to a very simple form of adaptive matched filter. Detecting tumors of variable size is accomplished by processing the images formed in a Laplacian pyramid, each of which contains a narrower range of tumor scales. We compare the performance of this pyramid technique with our earlier nonlinear detector, which detects small tumors according to their signature in curvature feature space, where 'curvature' is the local curvature of the image data when viewed as a relief map. Computed curvature values are mapped to a normalized significance space using a windowed t-statistic. The resulting test statistic is thresholded at a chosen level of significance to give a positive detection. Nonuniform anatomic background activity is effectively suppressed. This curvature detector works quite well over a large range of tumor scales, although not as well as the pyramid/adaptive matched filter scheme. None of the multiscale techniques tested perform at the level of the fixed scale detectors. Tests are performed using simulated tumors superimposed on clinical gamma-ray images.