Share Email Print
cover

Proceedings Paper

Fuzzy model-based body-wide anatomy recognition in medical images
Author(s): Jayaram K. Udupa; Dewey Odhner; Yubing Tong; Monica M. S. Matsumoto; Krzysztof C. Ciesielski; Pavithra Vaideeswaran; Victoria Ciesielski; Babak Saboury; Liming Zhao; Syedmehrdad Mohammadianrasanani; Drew Torigian
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

To make Quantitative Radiology a reality in routine radiological practice, computerized automatic anatomy recognition (AAR) becomes essential. Previously, we presented a fuzzy object modeling strategy for AAR. This paper presents several advances in this project including streamlined definition of open-ended anatomic objects, extension to multiple imaging modalities, and demonstration of the same AAR approach on multiple body regions. The AAR approach consists of the following steps: (a) Collecting image data for each population group G and body region B. (b) Delineating in these images the objects in B to be modeled. (c) Building Fuzzy Object Models (FOMs) for B. (d) Recognizing individual objects in a given image of B by using the models. (e) Delineating the recognized objects. (f) Implementing the computationally intensive steps in a graphics processing unit (GPU). Image data are collected for B and G from our existing patient image database. Fuzzy models for the individual objects are built and assembled into a model of B as per a chosen hierarchy of the objects in B. A global recognition strategy is used to determine the pose of the objects within a given image I following the hierarchy. The recognized pose is utilized to delineate the objects, also hierarchically. Based on three body regions tested utilizing both computed tomography (CT) and magnetic resonance (MR) imagery, recognition accuracy for non-sparse objects has been found to be generally sufficient ( 3 to 11 mm or 2-3 voxels) to yield delineation false positive (FP) and true positive (TP) values of < 5% and ≥ 90%, respectively. The sparse objects require further work to improve their recognition accuracy.

Paper Details

Date Published: 14 March 2013
PDF: 7 pages
Proc. SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 86712B (14 March 2013); doi: 10.1117/12.2007983
Show Author Affiliations
Jayaram K. Udupa, Medical Image Processing Group, Univ. of Pennsylvania (United States)
Dewey Odhner, Medical Image Processing Group, Univ. of Pennsylvania (United States)
Yubing Tong, Medical Image Processing Group, Univ. of Pennsylvania (United States)
Monica M. S. Matsumoto, Medical Image Processing Group, Univ. of Pennsylvania (United States)
Krzysztof C. Ciesielski, Medical Image Processing Group, Univ. of Pennsylvania (United States)
West Virginia Univ. (United States)
Pavithra Vaideeswaran, Medical Image Processing Group, Univ. of Pennsylvania (United States)
Victoria Ciesielski, Medical Image Processing Group, Univ. of Pennsylvania (United States)
Babak Saboury, Medical Image Processing Group, Univ. of Pennsylvania (United States)
Liming Zhao, Medical Image Processing Group, Univ. of Pennsylvania (United States)
Syedmehrdad Mohammadianrasanani, Medical Image Processing Group, Univ. of Pennsylvania (United States)
Drew Torigian, 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)

© SPIE. Terms of Use
Back to Top