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Proceedings Paper

Fast and effective similarity search in medical tumor databases using morphology
Author(s): Philip Korn; Nicholaos D. Sidiropoulos; Christos Faloutsos; Eliot L. Siegel; Zenon Protopapas
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Paper Abstract

We examine the problem of finding similar tumor shapes. The main contribution of this work is the proposal of a natural (dis-)similarity function for shape matching called the 'morphological distance'. This function has two desirable properties: a) it matches human perception of similarity, as we illustrate with precision/recall experiments; b) it can be lower-bounded by a set of features, leading to fast indexing for range queries and nearest neighbor queries. We use state-of-the-art methods from morphology both in defining our distance function and for feature extraction. In particular, we use the 'size-distribution', related to the 'pattern spectrum', to extract features from shapes. Following Jagadish and Faloutos et. al., we organize the n-d feature points in a spatial access method. We show that any Lp norm in the n-d space lower-bounds the morphological distance. This guarantees no false dismissals for range queries. In addition, we present a nearest neighbor algorithm that also guarantees no false dismissals. We implemented the method and tested it against a testbed of realistic tumor shapes generated by an established tumor- growth model. The response time of our method is up to 27 times faster than sequential scanning. Moreover, precision/recall experiments show that the proposed distance captures very well the dissimilarity as perceived by humans.

Paper Details

Date Published: 1 November 1996
PDF: 14 pages
Proc. SPIE 2916, Multimedia Storage and Archiving Systems, (1 November 1996); doi: 10.1117/12.257282
Show Author Affiliations
Philip Korn, Univ. of Maryland/College Park (United States)
Nicholaos D. Sidiropoulos, Univ. of Maryland/College Park (United States)
Christos Faloutsos, Univ. of Maryland/College Park (United States)
Eliot L. Siegel, Univ. of Maryland Medical School and Baltimore VA Medical Ctr. (United States)
Zenon Protopapas, Univ. of Maryland Medical School and Baltimore VA Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 2916:
Multimedia Storage and Archiving Systems
C.-C. Jay Kuo, Editor(s)

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