Patellar segmentation from 3D magnetic resonance images using guided recursive ray-tracing for edge pattern detection

Ruida Cheng, Jennifer N. Jackson, Evan S. McCreedy, William Gandler, J. J.F.A. Eijkenboom, M. Van Middelkoop, Matthew J. McAuliffe, Frances T. Sheehan

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationMedical Imaging 2016
Subtitle of host publicationBiomedical Applications in Molecular, Structural, and Functional Imaging
EditorsBarjor Gimi, Andrzej Krol
PublisherSPIE
ISBN (Electronic)9781510600232
DOIs
Publication statusPublished - 29 Mar 2016
EventMedical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging - San Diego, United States
Duration: 1 Mar 20163 Mar 2016

Publication series

SeriesProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9788
ISSN1605-7422

Conference

ConferenceMedical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging
Country/TerritoryUnited States
CitySan Diego
Period1/03/163/03/16

Bibliographical note

Publisher Copyright: © 2016 SPIE.

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