Finding Deformable Shapes by Correspondence-Free Instantiation and Registration of Statistical Shape Models View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2012

AUTHORS

Weiguo Xie , Steffen Schumann , Jochen Franke , Paul Alfred Grützner , Lutz-Peter Nolte , Guoyan Zheng

ABSTRACT

This paper addresses the problem of finding a deformable shape by instantiation and registration of a statistical shape model (SSM) to the observation. A correspondence-free approach based on expectation conditional maximization (ECM) framework is proposed, and a robust and efficient implementation is presented. Preliminary experiments conducted on SSM of both femur and pelvis resulted in an average mean reconstruction error of 2.7mm (50 sparse observation points) and of 1.1mm/1.0mm (100 sparse points, with/without noise) for femur, as well as a mean reconstruction error of 4.36mm for pelvis, which demonstrated the efficacy of the proposed approach. More... »

PAGES

258-265

References to SciGraph publications

  • 2004. Cadaver Validation of the Use of Ultrasound for 3D Model Instantiation of Bony Anatomy in Image Guided Orthopaedic Surgery in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2004
  • 2002. Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration in COMPUTER VISION — ECCV 2002
  • 2008. Localization of Pelvic Anatomical Coordinate System Using US/Atlas Registration for Total Hip Replacement in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2008
  • Book

    TITLE

    Machine Learning in Medical Imaging

    ISBN

    978-3-642-35427-4
    978-3-642-35428-1

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-35428-1_32

    DOI

    http://dx.doi.org/10.1007/978-3-642-35428-1_32

    DIMENSIONS

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