Geodesic Patch-Based Segmentation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Zehan Wang , Kanwal K. Bhatia , Ben Glocker , Antonio Marvao , Tim Dawes , Kazunari Misawa , Kensaku Mori , Daniel Rueckert

ABSTRACT

Label propagation has been shown to be effective in many automatic segmentation applications. However, its reliance on accurate image alignment means that segmentation results can be affected by any registration errors which occur. Patch-based methods relax this dependence by avoiding explicit one-to-one correspondence assumptions between images but are still limited by the search window size. Too small, and it does not account for enough registration error; too big, and it becomes more likely to select incorrect patches of similar appearance for label fusion. This paper presents a novel patch-based label propagation approach which uses relative geodesic distances to define patient-specific coordinate systems as spatial context to overcome this problem. The approach is evaluated on multi-organ segmentation of 20 cardiac MR images and 100 abdominal CT images, demonstrating competitive results. More... »

PAGES

666-73

References to SciGraph publications

  • 2013. Atlas Encoding by Randomized Forests for Efficient Label Propagation in ADVANCED INFORMATION SYSTEMS ENGINEERING
  • 2008. What Is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images? in COMPUTER VISION – ECCV 2008
  • 2008. GeoS: Geodesic Image Segmentation in COMPUTER VISION – ECCV 2008
  • 2012. Joint Classification-Regression Forests for Spatially Structured Multi-object Segmentation in COMPUTER VISION – ECCV 2012
  • 2013. Patch-Based Segmentation without Registration: Application to Knee MRI in MACHINE LEARNING IN MEDICAL IMAGING
  • 2013. Spatially Aware Patch-Based Segmentation (SAPS): An Alternative Patch-Based Segmentation Framework in MEDICAL COMPUTER VISION. RECOGNITION TECHNIQUES AND APPLICATIONS IN MEDICAL IMAGING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-10404-1_83

    DOI

    http://dx.doi.org/10.1007/978-3-319-10404-1_83

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1028311920

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/25333176


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