Fast Nearest Neighbor Search in Medical Image Databases

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1996

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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.

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.

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.

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