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dc.contributor.authorBanerjee, Imonen_US
dc.contributor.authorPaccini, Martinaen_US
dc.contributor.authorFerrari, Enricoen_US
dc.contributor.authorCATALANO, CHIARA EVAen_US
dc.contributor.authorBiasotti, Silviaen_US
dc.contributor.authorSpagnuolo, Michelaen_US
dc.contributor.editorAgus, Marco and Corsini, Massimiliano and Pintus, Ruggeroen_US
dc.date.accessioned2019-11-20T08:12:36Z
dc.date.available2019-11-20T08:12:36Z
dc.date.issued2019
dc.identifier.isbn978-3-03868-100-7
dc.identifier.issn2617-4855
dc.identifier.urihttps://doi.org/10.2312/stag.20191362
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/stag20191362
dc.description.abstractThis paper aims to examine the potential of 3D shape analysis integrated to machine learning techniques in supporting medical investigation. In particular, we introduce an approach specially designed for the characterisation of anatomical landmarks on patient-specific 3D carpal bone models represented as triangular meshes. Furthermore, to identify functional articulation regions, two novel district-based properties are defined. The performance of both state of the art and novel features has been evaluated in a machine learning setting to identify a set of significant anatomical landmarks on patient data. Experiments have been performed on a carpal dataset of 56 patient-specific 3D models that are segmented from T1 weighed magnetic resonance (MR) scans of healthy male subjects. Despite the typical large inter-patient shape variation within the training samples, our framework has achieved promising results.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectShape modeling
dc.subjectMachine learning approaches
dc.titleFeature-based Characterisation of Patient-specific 3D Anatomical Modelsen_US
dc.description.seriesinformationSmart Tools and Apps for Graphics - Eurographics Italian Chapter Conference
dc.description.sectionheadersFull Papers
dc.identifier.doi10.2312/stag.20191362
dc.identifier.pages41-50


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