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Proceedings Paper

Re-embedding vs. clustering as shape indexing strategies for medical image databases
Author(s): Xiaoning Qian; Hemant D. Tagare; Robert K. Fulbright
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Paper Abstract

Fast retrieval using complete or partial shapes of organs is an important functionality in medical image databases. Shapes of organs can be defined as points in shape spaces, which, in turn, are curved manifolds with a well-defined metric. In this paper, we experimentally compare two indexing techniques for shape spaces: first, we re-embed the shape space in a Euclidean space and use co-ordinate based indexing, and second, we used metric based hierarchical clustering for directly indexing shape space. The relative performances are evaluated with images from the NHANES II database of lumbar and cervical spine x-ray images on a shape similarity query. The experiments show that indexing using re-embedding is superior to cluster-based indexing.

Paper Details

Date Published: 15 April 2005
PDF: 8 pages
Proc. SPIE 5748, Medical Imaging 2005: PACS and Imaging Informatics, (15 April 2005); doi: 10.1117/12.595280
Show Author Affiliations
Xiaoning Qian, Yale Univ. (United States)
Hemant D. Tagare, Yale Univ. (United States)
Robert K. Fulbright, Yale Univ. (United States)

Published in SPIE Proceedings Vol. 5748:
Medical Imaging 2005: PACS and Imaging Informatics
Osman M. Ratib; Steven C. Horii, Editor(s)

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