Nonlinear Statistical Shape Modeling for Ankle Bone Segmentation Using a Novel Kernelized Robust PCA

  • Jingting MaEmail author
  • Anqi Wang
  • Feng Lin
  • Stefan Wesarg
  • Marius Erdt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)


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.


Statistical shape models Corrupted training data Nonlinear distribution Kernelized Robust PCA 



This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its International Research Centres in Singapore Funding Initiative.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jingting Ma
    • 1
    • 2
    Email author
  • Anqi Wang
    • 3
  • Feng Lin
    • 1
  • Stefan Wesarg
    • 3
  • Marius Erdt
    • 1
    • 2
  1. 1.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.Fraunhofer IDM@NTUNanyang Technological UniversitySingaporeSingapore
  3. 3.Visual Healthcare TechnologiesFraunhofer IGDDarmstadtGermany

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