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Image Gradient Based Shape Prior for the Segmentation of not that Spherical Structures

Steger, Sebastian ; Sakas, Georgios (2012)
Image Gradient Based Shape Prior for the Segmentation of not that Spherical Structures.
2012 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
doi: 10.1109/ISBI.2012.6235789
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

A popular method for the segmentation of somewhat spherical structures (e.g. certain types of tumors, lymph nodes, lung nodules) from 3D medical images is sending out radial rays from a central point and determining the most likely radius for each ray, resulting in a closed surface. Besides satisfying some image based criteria, a regularization term or shape prior typically ensures a smooth contour by preferring similar radii of neighboring rays. In this paper we show that the structures it is often applied to, are in fact not that spherical. We propose an alternate shape prior depending on the gradient direction, preferring smooth structures that are not necessarily spherical. We quantitatively evaluate the proposed shape prior with the traditionally used shape prior on a set of 49 lymph nodes from clinical images. A dice similarity coefficient improvement of 4 has been observed (0.80 vs. 0.77), yielding in segmentation accuracy close to manual segmentation (DSC of 0.83).

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2012
Autor(en): Steger, Sebastian ; Sakas, Georgios
Art des Eintrags: Bibliographie
Titel: Image Gradient Based Shape Prior for the Segmentation of not that Spherical Structures
Sprache: Englisch
Publikationsjahr: 2012
Verlag: IEEE Press, New York
Veranstaltungstitel: 2012 IEEE International Symposium on Biomedical Imaging: From Nano to Macro
DOI: 10.1109/ISBI.2012.6235789
Kurzbeschreibung (Abstract):

A popular method for the segmentation of somewhat spherical structures (e.g. certain types of tumors, lymph nodes, lung nodules) from 3D medical images is sending out radial rays from a central point and determining the most likely radius for each ray, resulting in a closed surface. Besides satisfying some image based criteria, a regularization term or shape prior typically ensures a smooth contour by preferring similar radii of neighboring rays. In this paper we show that the structures it is often applied to, are in fact not that spherical. We propose an alternate shape prior depending on the gradient direction, preferring smooth structures that are not necessarily spherical. We quantitatively evaluate the proposed shape prior with the traditionally used shape prior on a set of 49 lymph nodes from clinical images. A dice similarity coefficient improvement of 4 has been observed (0.80 vs. 0.77), yielding in segmentation accuracy close to manual segmentation (DSC of 0.83).

Freie Schlagworte: Business Field: Digital society, Research Area: Confluence of graphics and vision, Image segmentation, Shape priors
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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