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Superpixel and Entropy-Based Multi-atlas Fusion Framework for the Segmentation of X-ray Images

  • Dac Cong Tai Nguyen
  • Said BenameurEmail author
  • Max Mignotte
  • Frédéric Lavoie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9280)

Abstract

X-ray images segmentation can be useful to aid in accurate diagnosis or faithful 3D bone reconstruction but remains a challenging and complex task, particularly when dealing with large and complex anatomical structures such as the human pelvic bone. In this paper, we propose a multi-atlas fusion framework to automatically segment the human pelvic structure from 45 or 135-degree oblique X-ray radiographic images. Unlike most atlas-based approach, this method combines a data set of a priori segmented X-ray images of the human pelvis (or multi-atlas) to generate an adaptive superpixel map in order to take efficiently into account both the imaging pose variability along with the inter-patient (bone) shape non-linear variability. In addition, we propose a new label propagation or fusion step based on the variation of information criterion for integrating the multi-atlas information into the final consensus segmentation. We thoroughly evaluated the method on 30 manually segmented 45 or 135 degree oblique X-ray radiographic images data set by performing a leave-one-out study. Compared to the manual gold standard segmentations, the accuracy of our automatic segmentation approach is \(85\%\) which remains in the error range of manual segmentations due to the inter intra/observer variability.

Keywords

Consensus segmentation X-ray images Multi-atlas segmentation Variation of information based fusion step Superpixel map 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Dac Cong Tai Nguyen
    • 2
    • 3
  • Said Benameur
    • 2
    • 3
    Email author
  • Max Mignotte
    • 2
  • Frédéric Lavoie
    • 1
    • 3
  1. 1.Orthopedic Surgery DepartmentCentre Hospitalier de l’Université de Montréal (CHUM)MontréalCanada
  2. 2.Département d’Informatique et de Recherche Opérationnelle (DIRO)Université de MontréalQuébecCanada
  3. 3.Eiffel Medtech Inc.MontréalCanada

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