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Bone Segmentation in Metacarpophalangeal MR Data

  • Olga Kubassova
  • Roger D. Boyle
  • Mike Pyatnizkiy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3687)

Abstract

A robust, efficient segmentation algorithm for automatic segmentation of MR images of the metacarpophalangeal joint is presented. A preliminary segmentation detects bones in MR scans and uses histogram analysis, morphological operations and knowledge based rules to classify various tissues in the joint. The second part of the algorithm improves the segmentation mask and refines boundaries of bones using minimization of a sum of square deviations, automatic signal segmentation into an optimum number of segments, graph theory, and statistical analysis. The algorithm has been tested on 9 MR patient studies and detects 97% of all existing bones correctly with an average exceeding 80% mutual overlap between ground truth and detected regions

Keywords

Actual Boundary Magnetic Resonance Scan Signal Profile Boundary Pixel Magnetic Resonance Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Olga Kubassova
    • 1
  • Roger D. Boyle
    • 1
  • Mike Pyatnizkiy
    • 2
  1. 1.School of ComputingUniversity of LeedsLeedsUK
  2. 2.BiophysicsRussian State UniversityMoscowRussia

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