Edges or perceptible intensity transitions in digital imagery are identified from the zero-crossings of Laplacian of Gaussian (LOG) filtered images. Time or frequency-sampled LOG filters have been developed for the detection of edges in digital image data. The image is decomposed into overlapping subblocks and processed in the transform domain. In order to achieve accurate and efficient implementations, the discrete symmetric cosine transform (DSCT) of the input data is employed in conjunction with adaptive filters. The adaptive selection of the filter coefficients is based on the gradient criterion. For instance, in the case of the frequency-sampled LOG filter, the filter parameter is systematically varied to force the rejection of spurious edge classifications. In addition, the proposed algorithms easily extend to higher dimensions. This is useful where 3-D medical image data containing edge information has been corrupted by noise. This paper employs isotropic and non-isotropic filters to track edges in such images. The algorithm is implemented in 1-D, 2-D and 3-D and suitable examples will be presented.