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Knygos.lt nariams
- Išsiųsime per 12–18 d.d.
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- Autorius: Dagmar Kainmueller
- Leidėjas: Springer Vieweg
- Metai: 2014
- Puslapiai: 180
- ISBN-10: 3658070145
- ISBN-13: 9783658070144
- Formatas: 14.8 x 21 x 1.1 cm, minkšti viršeliai
- Kalba: Anglų
Deformable Meshes for Medical Image Segmentation (el. knyga) (skaityta knyga) | knygos.lt
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Aprašymas
Segmentation of anatomical structures in medical image data is an essential task in clinical practice. Dagmar Kainmueller introduces methods for accurate fully automatic segmentation of anatomical structures in 3D medical image data. The author's core methodological contribution is a novel deformation model that overcomes limitations of state-of-the-art Deformable Surface approaches, hence allowing for accurate segmentation of tip- and ridge-shaped features of anatomical structures. As for practical contributions, she proposes application-specific segmentation pipelines for a range of anatomical structures, together with thorough evaluations of segmentation accuracy on clinical image data. As compared to related work, these fully automatic pipelines allow for highly accurate segmentation of benchmark image data.
- Autorius: Dagmar Kainmueller
- Leidėjas: Springer Vieweg
- Metai: 2014
- Puslapiai: 180
- ISBN-10: 3658070145
- ISBN-13: 9783658070144
- Formatas: 14.8 x 21 x 1.1 cm, minkšti viršeliai
- Kalba: Anglų
Segmentation of anatomical structures in medical image data is an essential task in clinical practice. Dagmar Kainmueller introduces methods for accurate fully automatic segmentation of anatomical structures in 3D medical image data. The author's core methodological contribution is a novel deformation model that overcomes limitations of state-of-the-art Deformable Surface approaches, hence allowing for accurate segmentation of tip- and ridge-shaped features of anatomical structures. As for practical contributions, she proposes application-specific segmentation pipelines for a range of anatomical structures, together with thorough evaluations of segmentation accuracy on clinical image data. As compared to related work, these fully automatic pipelines allow for highly accurate segmentation of benchmark image data.
Atsiliepimai