(Enter summary)
Abstract: 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"... (Update)
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BibTeX entry: (Update)
Petrakis, E., Faloutsos, C. "Similarity Searching in Medical Image Databases". IEEE TKDE, 9 (3) 435-447, 1997. http://citeseer.ist.psu.edu/petrakis97similarity.html More
@article{ petrakis97similarity,
author = "Euripides G. M. Petrakis and Christos Faloutsos",
title = "Similarity Searching in Medical Image Databases",
journal = "Knowledge and Data Engineering",
volume = "9",
number = "3",
pages = "435-447",
year = "1997",
url = "citeseer.ist.psu.edu/petrakis97similarity.html" }
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