Fast Nearest Neighbor Search in Medical Image Databases.

@inproceedings{DBLP:conf/vldb/KornSFSP96,
  author    = {Flip Korn and
               Nikolaos Sidiropoulos and
               Christos Faloutsos and
               Eliot Siegel and
               Zenon Protopapas},
  editor    = {T. M. Vijayaraman and
               Alejandro P. Buchmann and
               C. Mohan and
               Nandlal L. Sarda},
  title     = {Fast Nearest Neighbor Search in Medical Image Databases},
  booktitle = {VLDB'96, Proceedings of 22th International Conference on Very
               Large Data Bases, September 3-6, 1996, Mumbai (Bombay), India},
  publisher = {Morgan Kaufmann},
  year      = {1996},
  isbn      = {1-55860-382-4},
  pages     = {215-226},
  ee        = {db/conf/vldb/KornSFSP96.html},
  crossref  = {DBLP:conf/vldb/96},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

We examine the problem of finding similar tumor shapes. Starting from a natural similarity function (the so-called `max morpho- logical 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_\infty 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 [13]. The experiments showed that our method is roughly an order of magnitude faster than the straighforward sequential scanning.

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.

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