A Riemannian Framework for Intrinsic Comparison of Closed Genus-Zero Shapes View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2015

AUTHORS

Boris A. Gutman , P. Thomas Fletcher , M. Jorge Cardoso , Greg M. Fleishman , Marco Lorenzi , Paul M. Thompson , Sebastien Ourselin

ABSTRACT

We present a framework for intrinsic comparison of surface metric structures and curvatures. This work parallels the work of Kurtek et al. on parameterization-invariant comparison of genus zero shapes. Here, instead of comparing the embedding of spherically parameterized surfaces in space, we focus on the first fundamental form. To ensure that the distance on spherical metric tensor fields is invariant to parameterization, we apply the conjugation-invariant metric arising from the L2 norm on symmetric positive definite matrices. As a reparameterization changes the metric tensor by a congruent Jacobian transform, this metric perfectly suits our purpose. The result is an intrinsic comparison of shape metric structure that does not depend on the specifics of a spherical mapping. Further, when restricted to tensors of fixed volume form, the manifold of metric tensor fields and its quotient of the group of unitary diffeomorphisms becomes a proper metric manifold that is geodesically complete. Exploiting this fact, and augmenting the metric with analogous metrics on curvatures, we derive a complete Riemannian framework for shape comparison and reconstruction. A by-product of our framework is a near-isometric and curvature-preserving mapping between surfaces. The correspondence is optimized using the fast spherical fluid algorithm. We validate our framework using several subcortical boundary surface models from the ADNI dataset. More... »

PAGES

205-18

References to SciGraph publications

  • 2014-09. Overview of the Geometries of Shape Spaces and Diffeomorphism Groups in JOURNAL OF MATHEMATICAL IMAGING AND VISION
  • 2012-08. Computing quasiconformal maps using an auxiliary metric and discrete curvature flow in NUMERISCHE MATHEMATIK
  • 2013. A Family of Fast Spherical Registration Algorithms for Cortical Shapes in MULTIMODAL BRAIN IMAGE ANALYSIS
  • 2003. Discrete Differential-Geometry Operators for Triangulated 2-Manifolds in VISUALIZATION AND MATHEMATICS III
  • 2010-11. The metric geometry of the manifold of Riemannian metrics over a closed manifold in CALCULUS OF VARIATIONS AND PARTIAL DIFFERENTIAL EQUATIONS
  • 2001-01. Group Actions, Homeomorphisms, and Matching: A General Framework in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-19992-4_16

    DOI

    http://dx.doi.org/10.1007/978-3-319-19992-4_16

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1006626125

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/26221675


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0101", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Pure Mathematics", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Mathematical Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Algorithms", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Alzheimer Disease", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Brain", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Computer Simulation", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Image Enhancement", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Image Interpretation, Computer-Assisted", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Magnetic Resonance Imaging", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Models, Statistical", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Pattern Recognition, Automated", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Reproducibility of Results", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Sensitivity and Specificity", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Subtraction Technique", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "name": [
                "Imaging Genetics Center, INI, University of Southern California", 
                "Center for Medical Image Computing, University College London"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Gutman", 
            "givenName": "Boris A.", 
            "id": "sg:person.01077655153.44", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01077655153.44"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Utah", 
              "id": "https://www.grid.ac/institutes/grid.223827.e", 
              "name": [
                "School of Computing, University of Utah"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Fletcher", 
            "givenName": "P. Thomas", 
            "id": "sg:person.01053501336.52", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01053501336.52"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "Center for Medical Image Computing, University College London"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Cardoso", 
            "givenName": "M. Jorge", 
            "id": "sg:person.01133623710.01", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01133623710.01"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "Imaging Genetics Center, INI, University of Southern California"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Fleishman", 
            "givenName": "Greg M.", 
            "id": "sg:person.01272463205.09", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01272463205.09"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "Center for Medical Image Computing, University College London"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lorenzi", 
            "givenName": "Marco", 
            "id": "sg:person.01027304674.80", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01027304674.80"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "Imaging Genetics Center, INI, University of Southern California"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Thompson", 
            "givenName": "Paul M.", 
            "id": "sg:person.013035660527.88", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013035660527.88"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "Center for Medical Image Computing, University College London"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ourselin", 
            "givenName": "Sebastien", 
            "id": "sg:person.0731256650.39", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0731256650.39"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1023/a:1011161132514", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001970622", 
              "https://doi.org/10.1023/a:1011161132514"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neuroimage.2008.10.040", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006623734"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00211-012-0446-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006842748", 
              "https://doi.org/10.1007/s00211-012-0446-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neuroimage.2013.04.018", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021954609"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neuroimage.2013.02.011", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022210267"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-02126-3_24", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023893852", 
              "https://doi.org/10.1007/978-3-319-02126-3_24"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neuroimage.2008.10.052", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024060645"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neuroimage.2007.11.041", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024590040"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00526-010-0323-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024634645", 
              "https://doi.org/10.1007/s00526-010-0323-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00526-010-0323-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024634645", 
              "https://doi.org/10.1007/s00526-010-0323-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10851-013-0490-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024861185", 
              "https://doi.org/10.1007/s10851-013-0490-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1090/s0002-9904-1968-12115-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025429878"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.sigpro.2005.12.018", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039520222"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-662-05105-4_2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044281423", 
              "https://doi.org/10.1007/978-3-662-05105-4_2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s1053-8119(03)00019-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045429851"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/83.536892", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061239494"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tmi.2004.831226", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061694616"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tmi.2007.892646", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061694997"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tmi.2009.2030797", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061695462"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tmi.2010.2099130", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061695681"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tmi.2014.2313812", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061696281"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/s0895479803436937", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062881878"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2015", 
        "datePublishedReg": "2015-01-01", 
        "description": "We present a framework for intrinsic comparison of surface metric structures and curvatures. This work parallels the work of Kurtek et al. on parameterization-invariant comparison of genus zero shapes. Here, instead of comparing the embedding of spherically parameterized surfaces in space, we focus on the first fundamental form. To ensure that the distance on spherical metric tensor fields is invariant to parameterization, we apply the conjugation-invariant metric arising from the L2 norm on symmetric positive definite matrices. As a reparameterization changes the metric tensor by a congruent Jacobian transform, this metric perfectly suits our purpose. The result is an intrinsic comparison of shape metric structure that does not depend on the specifics of a spherical mapping. Further, when restricted to tensors of fixed volume form, the manifold of metric tensor fields and its quotient of the group of unitary diffeomorphisms becomes a proper metric manifold that is geodesically complete. Exploiting this fact, and augmenting the metric with analogous metrics on curvatures, we derive a complete Riemannian framework for shape comparison and reconstruction. A by-product of our framework is a near-isometric and curvature-preserving mapping between surfaces. The correspondence is optimized using the fast spherical fluid algorithm. We validate our framework using several subcortical boundary surface models from the ADNI dataset.", 
        "editor": [
          {
            "familyName": "Ourselin", 
            "givenName": "Sebastien", 
            "type": "Person"
          }, 
          {
            "familyName": "Alexander", 
            "givenName": "Daniel C.", 
            "type": "Person"
          }, 
          {
            "familyName": "Westin", 
            "givenName": "Carl-Fredrik", 
            "type": "Person"
          }, 
          {
            "familyName": "Cardoso", 
            "givenName": "M. Jorge", 
            "type": "Person"
          }
        ], 
        "genre": "chapter", 
        "id": "sg:pub.10.1007/978-3-319-19992-4_16", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.3860228", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.2439676", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": {
          "isbn": [
            "978-3-319-19991-7", 
            "978-3-319-19992-4"
          ], 
          "name": "Information Processing in Medical Imaging", 
          "type": "Book"
        }, 
        "name": "A Riemannian Framework for Intrinsic Comparison of Closed Genus-Zero Shapes", 
        "pagination": "205-18", 
        "productId": [
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/978-3-319-19992-4_16"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "1f8f6bd07bca975ed6eb0a1d5ac7d21d0cdfd3f1f3742ed421750ae9658a2f64"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1006626125"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "26221675"
            ]
          }
        ], 
        "publisher": {
          "location": "Cham", 
          "name": "Springer International Publishing", 
          "type": "Organisation"
        }, 
        "sameAs": [
          "https://doi.org/10.1007/978-3-319-19992-4_16", 
          "https://app.dimensions.ai/details/publication/pub.1006626125"
        ], 
        "sdDataset": "chapters", 
        "sdDatePublished": "2019-04-15T20:03", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8687_00000247.jsonl", 
        "type": "Chapter", 
        "url": "http://link.springer.com/10.1007/978-3-319-19992-4_16"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-19992-4_16'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-19992-4_16'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-19992-4_16'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-19992-4_16'


     

    This table displays all metadata directly associated to this object as RDF triples.

    264 TRIPLES      23 PREDICATES      62 URIs      34 LITERALS      22 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/978-3-319-19992-4_16 schema:about N2217be9e1d674ff68af7df4d8d769890
    2 N2224024e84554f1bb558451f5a13f9db
    3 N2b489ea61c964b75bc6147bbe1984161
    4 N2d38f1a77c494fe698aba3cc781e7e72
    5 N4fa99186a8454f01b2b149607b7860de
    6 N587cbe79f97b458f9e6f00dbb3f965ed
    7 N5c6966bcf3a44ae28b52a5f72f76f64f
    8 N6d74c4d4a3ff4962ad250628a6d834a2
    9 N6fadf48be21642588fd50a0ff7cf542f
    10 N90a2e3a5b4904347b51b0f3eebe1ee51
    11 Nbe909ad10f694b91b4c5ab8c39954546
    12 Nd5559ebcc163422497b2a5058a6ee979
    13 Nfea6fa0d2c6644a58e741f3387923dfe
    14 anzsrc-for:01
    15 anzsrc-for:0101
    16 schema:author N4d0fc1c6cd414a20bd30ae6bf463b73e
    17 schema:citation sg:pub.10.1007/978-3-319-02126-3_24
    18 sg:pub.10.1007/978-3-662-05105-4_2
    19 sg:pub.10.1007/s00211-012-0446-z
    20 sg:pub.10.1007/s00526-010-0323-5
    21 sg:pub.10.1007/s10851-013-0490-z
    22 sg:pub.10.1023/a:1011161132514
    23 https://doi.org/10.1016/j.neuroimage.2007.11.041
    24 https://doi.org/10.1016/j.neuroimage.2008.10.040
    25 https://doi.org/10.1016/j.neuroimage.2008.10.052
    26 https://doi.org/10.1016/j.neuroimage.2013.02.011
    27 https://doi.org/10.1016/j.neuroimage.2013.04.018
    28 https://doi.org/10.1016/j.sigpro.2005.12.018
    29 https://doi.org/10.1016/s1053-8119(03)00019-3
    30 https://doi.org/10.1090/s0002-9904-1968-12115-9
    31 https://doi.org/10.1109/83.536892
    32 https://doi.org/10.1109/tmi.2004.831226
    33 https://doi.org/10.1109/tmi.2007.892646
    34 https://doi.org/10.1109/tmi.2009.2030797
    35 https://doi.org/10.1109/tmi.2010.2099130
    36 https://doi.org/10.1109/tmi.2014.2313812
    37 https://doi.org/10.1137/s0895479803436937
    38 schema:datePublished 2015
    39 schema:datePublishedReg 2015-01-01
    40 schema:description We present a framework for intrinsic comparison of surface metric structures and curvatures. This work parallels the work of Kurtek et al. on parameterization-invariant comparison of genus zero shapes. Here, instead of comparing the embedding of spherically parameterized surfaces in space, we focus on the first fundamental form. To ensure that the distance on spherical metric tensor fields is invariant to parameterization, we apply the conjugation-invariant metric arising from the L2 norm on symmetric positive definite matrices. As a reparameterization changes the metric tensor by a congruent Jacobian transform, this metric perfectly suits our purpose. The result is an intrinsic comparison of shape metric structure that does not depend on the specifics of a spherical mapping. Further, when restricted to tensors of fixed volume form, the manifold of metric tensor fields and its quotient of the group of unitary diffeomorphisms becomes a proper metric manifold that is geodesically complete. Exploiting this fact, and augmenting the metric with analogous metrics on curvatures, we derive a complete Riemannian framework for shape comparison and reconstruction. A by-product of our framework is a near-isometric and curvature-preserving mapping between surfaces. The correspondence is optimized using the fast spherical fluid algorithm. We validate our framework using several subcortical boundary surface models from the ADNI dataset.
    41 schema:editor N3f683adfe5764af2a6bbfab7a0b9651f
    42 schema:genre chapter
    43 schema:inLanguage en
    44 schema:isAccessibleForFree true
    45 schema:isPartOf Nbf9a513c08254c97adf705b2bbde2b4b
    46 schema:name A Riemannian Framework for Intrinsic Comparison of Closed Genus-Zero Shapes
    47 schema:pagination 205-18
    48 schema:productId N0006a15accca483aaf5be4751101ae9b
    49 N26de689e1706466b9c51facc35e0da22
    50 N9a418784d124475a8520ad89110d5c00
    51 Nfa85052cd13941afb8c707bdc44f2867
    52 schema:publisher N5c725f70bb64497896ddd02330d098ea
    53 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006626125
    54 https://doi.org/10.1007/978-3-319-19992-4_16
    55 schema:sdDatePublished 2019-04-15T20:03
    56 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    57 schema:sdPublisher Ne8a897d989824c488a252927ac2fbf9a
    58 schema:url http://link.springer.com/10.1007/978-3-319-19992-4_16
    59 sgo:license sg:explorer/license/
    60 sgo:sdDataset chapters
    61 rdf:type schema:Chapter
    62 N0006a15accca483aaf5be4751101ae9b schema:name dimensions_id
    63 schema:value pub.1006626125
    64 rdf:type schema:PropertyValue
    65 N049483d2808a408391281077758149bd schema:name Center for Medical Image Computing, University College London
    66 rdf:type schema:Organization
    67 N1f2a1160aa274ba7abdca126c4fd66e4 schema:name Center for Medical Image Computing, University College London
    68 rdf:type schema:Organization
    69 N2217be9e1d674ff68af7df4d8d769890 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    70 schema:name Computer Simulation
    71 rdf:type schema:DefinedTerm
    72 N2224024e84554f1bb558451f5a13f9db schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    73 schema:name Humans
    74 rdf:type schema:DefinedTerm
    75 N26de689e1706466b9c51facc35e0da22 schema:name pubmed_id
    76 schema:value 26221675
    77 rdf:type schema:PropertyValue
    78 N2b489ea61c964b75bc6147bbe1984161 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    79 schema:name Pattern Recognition, Automated
    80 rdf:type schema:DefinedTerm
    81 N2d38f1a77c494fe698aba3cc781e7e72 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    82 schema:name Brain
    83 rdf:type schema:DefinedTerm
    84 N3f683adfe5764af2a6bbfab7a0b9651f rdf:first Nef0ec5f125284b6fadfa06b488c193cb
    85 rdf:rest Nb858a855afd64a5a8e85ae5797a4bcb4
    86 N4d0fc1c6cd414a20bd30ae6bf463b73e rdf:first sg:person.01077655153.44
    87 rdf:rest Ne8468eb122b948a2bc527a5f462981ab
    88 N4fa99186a8454f01b2b149607b7860de schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    89 schema:name Magnetic Resonance Imaging
    90 rdf:type schema:DefinedTerm
    91 N53988051fc7044168f6d7a92b8607487 schema:name Center for Medical Image Computing, University College London
    92 rdf:type schema:Organization
    93 N57c22e71ea224a0db280e37c00b056d9 rdf:first N7db90844c70741fc90e02fdfa88b087b
    94 rdf:rest rdf:nil
    95 N587cbe79f97b458f9e6f00dbb3f965ed schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    96 schema:name Alzheimer Disease
    97 rdf:type schema:DefinedTerm
    98 N5c6966bcf3a44ae28b52a5f72f76f64f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    99 schema:name Image Interpretation, Computer-Assisted
    100 rdf:type schema:DefinedTerm
    101 N5c725f70bb64497896ddd02330d098ea schema:location Cham
    102 schema:name Springer International Publishing
    103 rdf:type schema:Organisation
    104 N5d3ea82088254337a9976e057c25238c rdf:first sg:person.0731256650.39
    105 rdf:rest rdf:nil
    106 N6d74c4d4a3ff4962ad250628a6d834a2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    107 schema:name Algorithms
    108 rdf:type schema:DefinedTerm
    109 N6fadf48be21642588fd50a0ff7cf542f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    110 schema:name Models, Statistical
    111 rdf:type schema:DefinedTerm
    112 N7db90844c70741fc90e02fdfa88b087b schema:familyName Cardoso
    113 schema:givenName M. Jorge
    114 rdf:type schema:Person
    115 N90a2e3a5b4904347b51b0f3eebe1ee51 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    116 schema:name Subtraction Technique
    117 rdf:type schema:DefinedTerm
    118 N97de6863d8104d0a85f4770ebe1611b9 rdf:first sg:person.01133623710.01
    119 rdf:rest Nbad8df72545e410cbd2e9e5a58312d7c
    120 N9a418784d124475a8520ad89110d5c00 schema:name readcube_id
    121 schema:value 1f8f6bd07bca975ed6eb0a1d5ac7d21d0cdfd3f1f3742ed421750ae9658a2f64
    122 rdf:type schema:PropertyValue
    123 N9cf1d06d8c01470fa045ebb385f6c5d6 schema:name Imaging Genetics Center, INI, University of Southern California
    124 rdf:type schema:Organization
    125 N9fb6fa84bd724c15b4b84db34ac15f27 schema:name Center for Medical Image Computing, University College London
    126 Imaging Genetics Center, INI, University of Southern California
    127 rdf:type schema:Organization
    128 Nb60f57ae49354e9e98d15b8ec6959e2c schema:familyName Westin
    129 schema:givenName Carl-Fredrik
    130 rdf:type schema:Person
    131 Nb858a855afd64a5a8e85ae5797a4bcb4 rdf:first Nc6c566605d534ba4a03b72a74425d9f2
    132 rdf:rest Nff25c73361084783abba415beb073cbc
    133 Nbad8df72545e410cbd2e9e5a58312d7c rdf:first sg:person.01272463205.09
    134 rdf:rest Nc43cfee1313e4d769e24dbd6d301f699
    135 Nbc6697791e564faeb97fabfa73b336d1 schema:name Imaging Genetics Center, INI, University of Southern California
    136 rdf:type schema:Organization
    137 Nbe909ad10f694b91b4c5ab8c39954546 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    138 schema:name Reproducibility of Results
    139 rdf:type schema:DefinedTerm
    140 Nbf9a513c08254c97adf705b2bbde2b4b schema:isbn 978-3-319-19991-7
    141 978-3-319-19992-4
    142 schema:name Information Processing in Medical Imaging
    143 rdf:type schema:Book
    144 Nc43cfee1313e4d769e24dbd6d301f699 rdf:first sg:person.01027304674.80
    145 rdf:rest Nf934307523c642e0aa1f618c30092139
    146 Nc6c566605d534ba4a03b72a74425d9f2 schema:familyName Alexander
    147 schema:givenName Daniel C.
    148 rdf:type schema:Person
    149 Nd5559ebcc163422497b2a5058a6ee979 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    150 schema:name Sensitivity and Specificity
    151 rdf:type schema:DefinedTerm
    152 Ne8468eb122b948a2bc527a5f462981ab rdf:first sg:person.01053501336.52
    153 rdf:rest N97de6863d8104d0a85f4770ebe1611b9
    154 Ne8a897d989824c488a252927ac2fbf9a schema:name Springer Nature - SN SciGraph project
    155 rdf:type schema:Organization
    156 Nef0ec5f125284b6fadfa06b488c193cb schema:familyName Ourselin
    157 schema:givenName Sebastien
    158 rdf:type schema:Person
    159 Nf934307523c642e0aa1f618c30092139 rdf:first sg:person.013035660527.88
    160 rdf:rest N5d3ea82088254337a9976e057c25238c
    161 Nfa85052cd13941afb8c707bdc44f2867 schema:name doi
    162 schema:value 10.1007/978-3-319-19992-4_16
    163 rdf:type schema:PropertyValue
    164 Nfea6fa0d2c6644a58e741f3387923dfe schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    165 schema:name Image Enhancement
    166 rdf:type schema:DefinedTerm
    167 Nff25c73361084783abba415beb073cbc rdf:first Nb60f57ae49354e9e98d15b8ec6959e2c
    168 rdf:rest N57c22e71ea224a0db280e37c00b056d9
    169 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
    170 schema:name Mathematical Sciences
    171 rdf:type schema:DefinedTerm
    172 anzsrc-for:0101 schema:inDefinedTermSet anzsrc-for:
    173 schema:name Pure Mathematics
    174 rdf:type schema:DefinedTerm
    175 sg:grant.2439676 http://pending.schema.org/fundedItem sg:pub.10.1007/978-3-319-19992-4_16
    176 rdf:type schema:MonetaryGrant
    177 sg:grant.3860228 http://pending.schema.org/fundedItem sg:pub.10.1007/978-3-319-19992-4_16
    178 rdf:type schema:MonetaryGrant
    179 sg:person.01027304674.80 schema:affiliation N1f2a1160aa274ba7abdca126c4fd66e4
    180 schema:familyName Lorenzi
    181 schema:givenName Marco
    182 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01027304674.80
    183 rdf:type schema:Person
    184 sg:person.01053501336.52 schema:affiliation https://www.grid.ac/institutes/grid.223827.e
    185 schema:familyName Fletcher
    186 schema:givenName P. Thomas
    187 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01053501336.52
    188 rdf:type schema:Person
    189 sg:person.01077655153.44 schema:affiliation N9fb6fa84bd724c15b4b84db34ac15f27
    190 schema:familyName Gutman
    191 schema:givenName Boris A.
    192 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01077655153.44
    193 rdf:type schema:Person
    194 sg:person.01133623710.01 schema:affiliation N53988051fc7044168f6d7a92b8607487
    195 schema:familyName Cardoso
    196 schema:givenName M. Jorge
    197 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01133623710.01
    198 rdf:type schema:Person
    199 sg:person.01272463205.09 schema:affiliation Nbc6697791e564faeb97fabfa73b336d1
    200 schema:familyName Fleishman
    201 schema:givenName Greg M.
    202 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01272463205.09
    203 rdf:type schema:Person
    204 sg:person.013035660527.88 schema:affiliation N9cf1d06d8c01470fa045ebb385f6c5d6
    205 schema:familyName Thompson
    206 schema:givenName Paul M.
    207 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013035660527.88
    208 rdf:type schema:Person
    209 sg:person.0731256650.39 schema:affiliation N049483d2808a408391281077758149bd
    210 schema:familyName Ourselin
    211 schema:givenName Sebastien
    212 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0731256650.39
    213 rdf:type schema:Person
    214 sg:pub.10.1007/978-3-319-02126-3_24 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023893852
    215 https://doi.org/10.1007/978-3-319-02126-3_24
    216 rdf:type schema:CreativeWork
    217 sg:pub.10.1007/978-3-662-05105-4_2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044281423
    218 https://doi.org/10.1007/978-3-662-05105-4_2
    219 rdf:type schema:CreativeWork
    220 sg:pub.10.1007/s00211-012-0446-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1006842748
    221 https://doi.org/10.1007/s00211-012-0446-z
    222 rdf:type schema:CreativeWork
    223 sg:pub.10.1007/s00526-010-0323-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024634645
    224 https://doi.org/10.1007/s00526-010-0323-5
    225 rdf:type schema:CreativeWork
    226 sg:pub.10.1007/s10851-013-0490-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1024861185
    227 https://doi.org/10.1007/s10851-013-0490-z
    228 rdf:type schema:CreativeWork
    229 sg:pub.10.1023/a:1011161132514 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001970622
    230 https://doi.org/10.1023/a:1011161132514
    231 rdf:type schema:CreativeWork
    232 https://doi.org/10.1016/j.neuroimage.2007.11.041 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024590040
    233 rdf:type schema:CreativeWork
    234 https://doi.org/10.1016/j.neuroimage.2008.10.040 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006623734
    235 rdf:type schema:CreativeWork
    236 https://doi.org/10.1016/j.neuroimage.2008.10.052 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024060645
    237 rdf:type schema:CreativeWork
    238 https://doi.org/10.1016/j.neuroimage.2013.02.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022210267
    239 rdf:type schema:CreativeWork
    240 https://doi.org/10.1016/j.neuroimage.2013.04.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021954609
    241 rdf:type schema:CreativeWork
    242 https://doi.org/10.1016/j.sigpro.2005.12.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039520222
    243 rdf:type schema:CreativeWork
    244 https://doi.org/10.1016/s1053-8119(03)00019-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045429851
    245 rdf:type schema:CreativeWork
    246 https://doi.org/10.1090/s0002-9904-1968-12115-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025429878
    247 rdf:type schema:CreativeWork
    248 https://doi.org/10.1109/83.536892 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061239494
    249 rdf:type schema:CreativeWork
    250 https://doi.org/10.1109/tmi.2004.831226 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061694616
    251 rdf:type schema:CreativeWork
    252 https://doi.org/10.1109/tmi.2007.892646 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061694997
    253 rdf:type schema:CreativeWork
    254 https://doi.org/10.1109/tmi.2009.2030797 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061695462
    255 rdf:type schema:CreativeWork
    256 https://doi.org/10.1109/tmi.2010.2099130 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061695681
    257 rdf:type schema:CreativeWork
    258 https://doi.org/10.1109/tmi.2014.2313812 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061696281
    259 rdf:type schema:CreativeWork
    260 https://doi.org/10.1137/s0895479803436937 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062881878
    261 rdf:type schema:CreativeWork
    262 https://www.grid.ac/institutes/grid.223827.e schema:alternateName University of Utah
    263 schema:name School of Computing, University of Utah
    264 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...