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1 November 1996 Fast and effective similarity search in medical tumor databases using morphology
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Proceedings Volume 2916, Multimedia Storage and Archiving Systems; (1996)
Event: Photonics East '96, 1996, Boston, MA, United States
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.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Philip Korn, Nicholaos D. Sidiropoulos, Christos Faloutsos, Eliot L. Siegel, and Zenon Protopapas "Fast and effective similarity search in medical tumor databases using morphology", Proc. SPIE 2916, Multimedia Storage and Archiving Systems, (1 November 1996);

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