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Proceedings Paper

Optimal hierarchies for fuzzy object models
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Paper Abstract

In radiologic clinical practice, the analysis underlying image examinations are qualitative, descriptive, and to some extent subjective. Quantitative radiology (QR) is valuable in clinical radiology. Computerized automatic anatomy recognition (AAR) is an essential step toward that goal. AAR is a body-wide organ recognition strategy. The AAR framework is based on fuzzy object models (FOMs) wherein the models for the different objects are encoded in a hierarchy. We investigated ways of optimally designing the hierarchy tree while building the models. The hierarchy among the objects is a core concept of AAR. The parent-offspring relationships have two main purposes in this context: (i) to bring into AAR more understanding and knowledge about the form, geography, and relationships among objects, and (ii) to foster guidance to object recognition and object delineation. In this approach, the relationship among objects is represented by a graph, where the vertices are the objects (organs) and the edges connect all pairs of vertices into a complete graph. Each pair of objects is assigned a weight described by the spatial distance between them, their intensity profile differences, and their correlation in size, all estimated over a population. The optimal hierarchy tree is obtained by the shortest-path algorithm as an optimal spanning tree. To evaluate the optimal hierarchies, we have performed some preliminary tests involving the subsequent recognition step. The body region used for initial investigation was the thorax.

Paper Details

Date Published: 15 March 2013
PDF: 7 pages
Proc. SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 86712C (15 March 2013); doi: 10.1117/12.2007604
Show Author Affiliations
Monica M. S. Matsumoto, Medical Image Processing Group, Univ. of Pennsylvania (United States)
Jayaram K. Udupa, Medical Image Processing Group, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 8671:
Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling
David R. Holmes; Ziv R. Yaniv, Editor(s)

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