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Bone Age Assessment Using the Classifying Generalized Hough Transform

  • Ferdinand Hahmann
  • Inga Berger
  • Heike Ruppertshofen
  • Thomas Deserno
  • Hauke Schramm
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8142)

Abstract

A theoretical description and experimental validation of the Classifying Generalized Hough Transform (CGHT) is presented. This general image classification technique is based on a discriminative training procedure that jointly estimates concurrent class-dependent shape models for usage in a GHT voting procedure. The basic approach is extended by a coarse-to-fine classification strategy and a simple classifier combination technique for a combined decision on several regions of interest in a given image. The framework is successfully applied to the task of automatic bone age assessment and produces comparable results to other state-of-the-art techniques on a public database. For the most difficult age range of 9 to 16 years the automatic system achieves a mean error of 0.8 years compared to the average rating of two physicians. Unlike most other image classification techniques, the trained CGHT models can be visually interpreted, unveiling the most relevant anatomical structures for class discrimination.

Keywords

Training Image Skeletal Maturity Vote Procedure Shape Variability Combine Decision 
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 2013

Authors and Affiliations

  • Ferdinand Hahmann
    • 1
  • Inga Berger
    • 1
  • Heike Ruppertshofen
    • 2
  • Thomas Deserno
    • 3
  • Hauke Schramm
    • 1
  1. 1.University of Applied Sciences KielGermany
  2. 2.Philips Technologie GmbHGermany
  3. 3.Department of Medical InformaticsRWTH Aachen UniversityGermany

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