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Segmentation of the Facial Nerve using Active Appearance Models

Fuchs, Konstantin (2012)
Segmentation of the Facial Nerve using Active Appearance Models.
Technische Universität Darmstadt
Masterarbeit, Bibliographie

Kurzbeschreibung (Abstract)

A large-scale invasive approach is today's standard technique for temporal bone surgery, although it is time-consuming and causes significant tissue damage. One could overcome these disadvantages with a minimally-invasive operation method, where the whole surgery is performed through three drill canals. In order not to damage vital structures by drilling the canals, a detailed and exact operation planning is crucial, including the segmentation of all critical structures in preoperative computer tomographic data. The facial nerve is one of the important collision structures in the temporal bone region. Its segmentation is a challenging task because of its small size, its weak contrast to adjacent structures and large inter-patient variations. In this thesis, a semi-automatic two-step algorithm for the segmentation of the facial nerve is presented. We propose an Active Appearance Model based method for the extraction of the facial nerve's centerline and evaluate four different texture descriptors in this context. For the subsequent full structure segmentation, we introduce a ray-based approach that uses the centerline for initialization. The approaches for both centerline extraction and full structure segmentation yield reliable results. They show the best segmentation quality using an introduced intensity histogram as texture descriptor.

Typ des Eintrags: Masterarbeit
Erschienen: 2012
Autor(en): Fuchs, Konstantin
Art des Eintrags: Bibliographie
Titel: Segmentation of the Facial Nerve using Active Appearance Models
Sprache: Englisch
Publikationsjahr: 2012
Kurzbeschreibung (Abstract):

A large-scale invasive approach is today's standard technique for temporal bone surgery, although it is time-consuming and causes significant tissue damage. One could overcome these disadvantages with a minimally-invasive operation method, where the whole surgery is performed through three drill canals. In order not to damage vital structures by drilling the canals, a detailed and exact operation planning is crucial, including the segmentation of all critical structures in preoperative computer tomographic data. The facial nerve is one of the important collision structures in the temporal bone region. Its segmentation is a challenging task because of its small size, its weak contrast to adjacent structures and large inter-patient variations. In this thesis, a semi-automatic two-step algorithm for the segmentation of the facial nerve is presented. We propose an Active Appearance Model based method for the extraction of the facial nerve's centerline and evaluate four different texture descriptors in this context. For the subsequent full structure segmentation, we introduce a ray-based approach that uses the centerline for initialization. The approaches for both centerline extraction and full structure segmentation yield reliable results. They show the best segmentation quality using an introduced intensity histogram as texture descriptor.

Freie Schlagworte: Forschungsgruppe Medical Computing (MECO), Statistical shape models (SSM), Active appearance models, Segmentation, Medical image processing, 3D Medical data
Zusätzliche Informationen:

55 p.

Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Graphisch-Interaktive Systeme
Hinterlegungsdatum: 12 Nov 2018 11:16
Letzte Änderung: 12 Nov 2018 11:16
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