Date of Original Version
Copyright © 1996 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.
Abstract or Description
We examine the problem of finding similar tumor shapes. Starting from a natural similarity function (the so-called `max morphological distance'), we showed how to lower-bound it and how to search for nearest neighbors in large collections of tumor-like shapes.
Specifically, we used state-of-the-art concepts from morphology, namely the `pattern spectrum' of a shape, to map each shape to a point in n-dimensional space. Following [16, 30], we organized the n-d points in an R-tree. We showed that the L∞ (=max) norm in the n-d space lower-bounds the actual distance. This guarantees no false dismissals for range queries. In addition, we developed a nearest-neighbor algorithm that also guarantees no false dismissals.
Finally, we implemented the method, and we tested it against a testbed of realistic tumor shapes, using an established tumor- growth model of Murray Eden . The experiments showed that our method is roughly an order of magnitude faster than the straightforward sequential scanning.
Proceedings of 22th International Conference on Very Large Data Bases.