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  4. Nonlinear statistical shape modeling for ankle bone segmentation using a novel kernelized robust PCA
 
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2017
  • Konferenzbeitrag

Titel

Nonlinear statistical shape modeling for ankle bone segmentation using a novel kernelized robust PCA

Abstract
Statistical shape models (SSMs) are widely employed in medical image segmentation. However, an inferior SSM will degenerate the quality of segmentations. It is challenging to derive an efficient model because: (1) often the training datasets are corrupted by noise and/or artifacts; (2) conventional SSM is not capable to capture nonlinear variabilities of a population of shape. Addressing these challenges, this work aims to create SSMs that are not only robust to abnormal training data but also satisfied with nonlinear distribution. As Robust PCA is an efficient tool to seek a clean low-rank linear subspace, a novel kernelized Robust PCA (KRPCA) is proposed to cope with nonlinear distribution for statistical shape modeling. In evaluation, the built nonlinear model is used in ankle bone segmentation where 9 bones are separately distributed. Evaluation results show that the model built with KRPCA has a significantly higher quality than other state-of-the-art methods.
Author(s)
Ma, Jingting
Fraunhofer Singapore
Wang, Anqi
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Lin, Feng
Nanyang Technological University, Singapore
Wesarg, Stefan
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Erdt, Marius
Fraunhofer Singapore
Hauptwerk
Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Konferenz
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017
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DOI
10.1007/978-3-319-66182-7_16
Language
Englisch
google-scholar
IGD
Singapore
Tags
  • statistical shape mod...

  • medical imaging

  • Lead Topic: Individua...

  • Research Line: Comput...

  • Research Line: Modeli...

  • medical image process...

  • kernel principal comp...

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