Automatic anatomy recognition via fuzzy object models


J.K. Udupa, D. Odhner, A.X. Falcao, Krzysztof Chris Ciesielski, P.A.V. Miranda, M. Matsumoto, G.J. Grevera, B. Saboury, and D.A. Torigian

Medical Imaging 2012: Image Processing, SPIE Proceedings 8316, 2012.

To make Quantitative Radiology a reality in routine radiological practice, computerized automatic anatomy recognition (AAR) during radiological image reading becomes essential. As part of this larger goal, last year at this conference we presented a novel fuzzy strategy for building body-wide group-wise anatomic models. In the present paper, we describe the further advances made in fuzzy modeling and the algorithms and results achieved for AAR by using the fuzzy models. The proposed AAR approach consists of three distinct steps: (a) Building fuzzy object models (FOMs) for each population group G. (b) By using the FOMs to recognize the individual objects in any given patient image I under group G. (c) To delineate the recognized objects in I. This paper will focus mostly on (b).

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Last modified March 25, 2012.