Fast Nearest Neighbor Search in Medical Image Databases

We examine the problem of finding similar tumor shapes. Starting from a natural similarity function (the so-called `max 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 `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∞ (=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 [13]. The experiments showed that our method is roughly an order of magnitude faster than the straightforward sequential scanning.