We propose a method to handle approximate searching by image content in medical image databases. Image content is represented by attributed relational graphs holding features of objects and relationships between objects. The method relies on the assumption that a fixed number of "labeled" or "expected" objects (e.g., "heart", "lungs", etc.) are common in all images of a given application domain in addition to a variable number of "unexpected" or "unlabeled" objects (e.g., "tumor", "hematoma", etc.). The method can answer queries by example, such as "find all X-rays that are similar to Smith's X-ray". The stored images are mapped to points in a multidimensional space and are indexed using state-of-the-art database methods (R-trees). The proposed method has several desirable properties: (a) Database search is approximate, so that all images up to a prespecified degree of similarity (tolerance) are retrieved. (b) It has no "false dismissals" (i.e., all images qualifying query selection criteria are retrieved). (c) It is much faster than sequential scanning for searching in the main memory and on the disk (i.e., by up to an order of magnitude), thus scaling-up well for large databases.
['Emmanuel Petrakis', 'A. Faloutsos']
Similarity searching in medical image databases