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Proceedings Paper

Patellar segmentation from 3D magnetic resonance images using guided recursive ray-tracing for edge pattern detection
Author(s): Ruida Cheng; Jennifer N. Jackson; Evan S. McCreedy; William Gandler; J. J. F. A. Eijkenboom; M. van Middelkoop; Matthew J. McAuliffe; Frances T. Sheehan
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Paper Abstract

The paper presents an automatic segmentation methodology for the patellar bone, based on 3D gradient recalled echo and gradient recalled echo with fat suppression magnetic resonance images. Constricted search space outlines are incorporated into recursive ray-tracing to segment the outer cortical bone. A statistical analysis based on the dependence of information in adjacent slices is used to limit the search in each image to between an outer and inner search region. A section based recursive ray-tracing mechanism is used to skip inner noise regions and detect the edge boundary. The proposed method achieves higher segmentation accuracy (0.23mm) than the current state-of-the-art methods with the average dice similarity coefficient of 96.0% (SD 1.3%) agreement between the auto-segmentation and ground truth surfaces.

Paper Details

Date Published: 29 March 2016
PDF: 9 pages
Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 97880C (29 March 2016); doi: 10.1117/12.2217232
Show Author Affiliations
Ruida Cheng, National Institutes of Health (United States)
Jennifer N. Jackson, National Institutes of Health (United States)
Evan S. McCreedy, National Institutes of Health (United States)
William Gandler, National Institutes of Health (United States)
J. J. F. A. Eijkenboom, Erasmus MC (Netherlands)
M. van Middelkoop, Erasmus MC (Netherlands)
Matthew J. McAuliffe, National Institutes of Health (United States)
Frances T. Sheehan, National Institutes of Health (United States)


Published in SPIE Proceedings Vol. 9788:
Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

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