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Accurate Bone Segmentation in 2D Radiographs Using Fully Automatic Shape Model Matching Based On Regression-Voting

  • Claudia Lindner
  • Shankar Thiagarajah
  • J. Mark Wilkinson
  • arcOGEN Consortium
  • Gillian A. Wallis
  • Tim F. Cootes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)

Abstract

Recent work has shown that using Random Forests (RFs) to vote for the optimal position of model feature points leads to robust and accurate shape model matching. This paper applies RF regression-voting as part of a fully automatic shape model matching (FASMM) system to three different radiograph segmentation problems: the proximal femur, the bones of the knee joint and the joints of the hand. We investigate why this approach works so well and demonstrate that the performance comes from a combination of three properties: (i) The integration of votes from multiple regions around the model point. (ii) The combination of multiple independent votes from each tree. (iii) The use of a coarse to fine strategy. We show that each property can improve performance, and that the best performance comes from using all three. We demonstrate that FASMM based on RF regression-voting generalises well across application areas, achieving state of the art performance in each of the three segmentation problems. This FASMM system provides an accurate and time-efficient way for the segmentation of bony structures in radiographs.

Keywords

Computational anatomy Random Forests Constrained Local Models statistical shape models bone segmentation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Claudia Lindner
    • 1
  • Shankar Thiagarajah
    • 2
  • J. Mark Wilkinson
    • 2
  • arcOGEN Consortium
    • 1
  • Gillian A. Wallis
    • 3
  • Tim F. Cootes
    • 1
  1. 1.Centre for Imaging SciencesUniversity of ManchesterUK
  2. 2.Department of Human MetabolismUniversity of SheffieldUK
  3. 3.Wellcome Trust Centre for Cell Matrix ResearchUniversity of ManchesterUK

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