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Kernel Centered Alignment Supervised Metric for Multi-Atlas Segmentation

  • Mauricio Orbes-ArteagaEmail author
  • David Cárdenas-Peña
  • Mauricio A. Álvarez
  • Alvaro A. Orozco
  • Germán Castellanos-Dominguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9279)

Abstract

Recently multi-atlas based methods have been used for supporting brain structure segmentation. These approaches encode the shape variability on a given population and provide prior information. However, the accuracy on the segmentation depend on the capability of the each atlas on the dataset to propagate the labels to the target image. In this sense, the selection of the most relevant atlases becomes an important task. In this paper, a new locally-weighted criterion is proposed to highlight spatial correspondences between images, aiming to enhance multi-atlas based segmentation results. Our proposal combines the spatial correspondences by a linear weighted combination and uses the kernel centered alignment criterion to find the best weight combination. The proposal is tested in an MRI segmentation task for state of the art image metrics as Mean Squares and Mutual Information and it is compared against other weighting criterion methods. Obtained results show that our approach outperforms the baseline methods providing a more suitable atlas selection and improving the segmentation of ganglia basal structures.

Keywords

Magnetic resonance imaging Image similarity metric Multi-atlas segmentation Template selection 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mauricio Orbes-Arteaga
    • 1
    Email author
  • David Cárdenas-Peña
    • 1
  • Mauricio A. Álvarez
    • 2
  • Alvaro A. Orozco
    • 2
  • Germán Castellanos-Dominguez
    • 1
  1. 1.Universidad Nacional de ColombiaManizalesColombia
  2. 2.Universidad Tecnológica de PereiraPereiraColombia

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