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 morpholog- ical 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 \cite{Faloutsos94Fast,Jagadish91Retrieval}, we organized the $n$-d points in an R-tree. We showed that the $L_infty$ (= 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 \cite{Eden:61}. The experiments showed that our method is up to 27 times faster than straightfor- ward sequential scanning. (Also cross-referenced as UMIACS-TR-96-17

Fast and Effective Similarity Search in Medical Tumor Databases Using Morphology

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 Faloutsos et al., we organize the n-d feature points in a spatial access method (SAM). We show that any L p 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 neig..

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 show how to lower-bound it and how to search for nearest neighbors in large collections of tumor-like shapes. Specifically, we use 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 organize the n-d points in an R-tree. We show that the L1 (= max) norm in the n-d space lower-bounds the actual distance. This guarantees no false dismissals for range queries. In addition, we present a nearest neighbor algorithm that also guarantees no false dismissals. Finally, we implemented the method and tested it against a testbed of realistic tumor shapes, using an established tumor-growth model of Murray Eden[13]. The experiments Permission to copy without fee all or part of this material is granted provided that the copies ..

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