Fully Automatic X-Ray Image Segmentation via Joint Estimation of Image Displacements View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2013

AUTHORS

Cheng Chen , Weiguo Xie , Jochen Franke , Paul A Grützner , Lutz-P Nolte , Guoyan Zheng

ABSTRACT

We propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. Our algorithm works by estimating the displacements from image patches to the (unknown) landmark positions and then integrating them via voting. The fundamental contribution is that, we jointly estimate the displacements from all patches to multiple landmarks together, by considering not only the training data but also geometric constraints on the test image. The various constraints constitute a convex objective function that can be solved efficiently. Validated on three challenging datasets, our method achieves high accuracy in landmark detection, and, combined with statistical shape model, gives a better performance in shape segmentation compared to the state-of-the-art methods. More... »

PAGES

227-34

References to SciGraph publications

  • 2011. Fast Multiple Organ Detection and Localization in Whole-Body MR Dixon Sequences in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2011
  • 2011. Regression Forests for Efficient Anatomy Detection and Localization in CT Studies in MEDICAL COMPUTER VISION. RECOGNITION TECHNIQUES AND APPLICATIONS IN MEDICAL IMAGING
  • 2012. Accurate Fully Automatic Femur Segmentation in Pelvic Radiographs Using Regression Voting in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2012
  • 2007. Shape Regression Machine in INFORMATION PROCESSING IN MEDICAL IMAGING
  • 2005. Automatic Extraction of Femur Contours from Hip X-Ray Images in COMPUTER VISION FOR BIOMEDICAL IMAGE APPLICATIONS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-40760-4_29

    DOI

    http://dx.doi.org/10.1007/978-3-642-40760-4_29

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1000372326

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/24505765


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