@inproceedings{DBLP:conf/vldb/KornSFSP96, author = {Flip Korn and Nikolaos Sidiropoulos and Christos Faloutsos and Eliot L. 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 = {http://www.vldb.org/conf/1996/P215.PDF}, crossref = {DBLP:conf/vldb/96}, bibsource = {DBLP, http://dblp.uni-trier.de} }

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
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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.*

Contents

- From SunSITE Central Europe (Aachen, Germany)
- From CS Dept., University Trier (Germany)

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