Abstract-We investigate the problem of retrieving similar shapes from a large database; in particular, we focus on medical tumor shapes ("Find tumors that are similar to a given pattern."). We use a natural similarity function for shape-matching, based on concepts from mathematical morphology, and we show how it can be lower-bounded by a set of shape features for safely pruning candidates, thus giving fast and correct output. These features can be organized in a spatial access method, leading to fast indexing for range queries and nearest-neighbor queries. In addition to the lower-bounding, our second contribution is the design of a fast algorithm for nearest-neighbor search, achieving significant speedup while provably guaranteeing correctness. Our experiments demonstrate that roughly 90 percent of the candidates can be pruned using these techniques, resulting in up to 27 times better performance compared to sequential scan.Index Terms-Content-based retrieval, multimedia indexing, mathematical morphology, pattern spectrum.
Endobronchial location and lower lobe predominance were the most common characteristics of overlooked lung cancer at CT. The presence of unrelated major abnormalities at CT may also have contributed to failure to diagnose the tumor.
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