Managing massive databases of scientific images requires new techniques that address indexing visual content, providing adequate browse capabilities, and facilitating querying by image content. Subband decomposition of image data using wavelet filters is offered as an aid to solving each of these problems. It is fundamental to a vidual indexing scheme that constructs a pruned tree of significant subbands as a first level of index. Significance is determined by feature vectors including Markov random field statistics, in addition to more common measures of energy and entropy. Features are retained at the nodes of the pruned subband tree as a second level of index. Query images, indexed in the same manner as database images, are compared as closely as desired to database indexes. Browse images for matching images are transmitted to the user in the form of subband coefficients, which constitute the third level of index. These coefficients, chosen for their unique significance to the indexed image, are likely to contain valuable information for the subject area specialist. This paper presents the indexing scheme in detail, and reports some preliminary results of selecting subbands for reconstruction as browse images based on their significance for indexing purposes.