Fast Nearest Neighbor Search in Medical Image Databases
| dc.contributor.author | Korn, Flip | en_US |
| dc.contributor.author | Sidiropoulos, N. | en_US |
| dc.contributor.author | Faloutsos, Christos | en_US |
| dc.contributor.department | ISR | en_US |
| dc.date.accessioned | 2007-05-23T10:01:22Z | |
| dc.date.available | 2007-05-23T10:01:22Z | |
| dc.date.issued | 1996 | en_US |
| dc.description.abstract | We examine the problem of finding similar tumor shapes. Starting from a natural similarity function (the so-called ax morphological distance'), we showed how to lower-bound it and how to search for nearest neighbors in large collections of tumor- like shapes.<P>Specifically, we used state-of-the-art concepts from morphology, namely the attern spectrum' of a shape, to map each shape to a point in n-dimensional space. Following [19, 36], 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.<P>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 [15]. The experiments showed that our method is up to 27 times faster than straightforward sequential scanning.<P> | en_US |
| dc.format.extent | 1364727 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1903/5743 | |
| dc.language.iso | en_US | en_US |
| dc.relation.ispartofseries | ISR; TR 1996-13 | en_US |
| dc.subject | signal processing | en_US |
| dc.subject | databases | en_US |
| dc.subject | feature extraction | en_US |
| dc.subject | medical information systems | en_US |
| dc.subject | access methods | en_US |
| dc.subject | Systems Integration Methodology | en_US |
| dc.title | Fast Nearest Neighbor Search in Medical Image Databases | en_US |
| dc.type | Technical Report | en_US |
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