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Bone age assessment in young children using automatic carpal bone feature extraction and support vector regression.

Krit Somkantha1, Nipon Theera-Umpon, Sansanee Auephanwiriyakul

  • 1Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand.

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|February 25, 2011
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This study introduces an edge-following technique for carpal bone boundary extraction, improving automatic bone age assessment in children. Support vector regression (SVR) demonstrated superior accuracy compared to neural network regression (NNR).

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Area of Science:

  • Medical Imaging
  • Radiology
  • Biomedical Engineering

Background:

  • Accurate bone age assessment is crucial for pediatric growth evaluation.
  • Poor contrast in carpal bone X-rays complicates automated boundary extraction.
  • Existing methods may lack precision in identifying carpal bone edges.

Purpose of the Study:

  • To develop and evaluate an edge-following technique for precise carpal bone boundary extraction.
  • To apply this technique for automated bone age assessment in young children.
  • To compare the performance of Support Vector Regression (SVR) against Neural Network Regression (NNR) for this task.

Main Methods:

  • An edge-following technique utilizing vector image models and edge maps for boundary extraction.
  • Calculation of five distinct features from extracted carpal bone boundaries.
  • Inputting these features into a Support Vector Regression (SVR) model for bone age prediction.
  • Comparison with Neural Network Regression (NNR) using 180 digital hand atlas images (0-6 years).
  • Leave-one-out cross-validation for performance evaluation.

Main Results:

  • The proposed edge-following technique successfully extracts carpal bone boundaries from low-contrast X-ray images.
  • Support Vector Regression (SVR) achieved higher accuracy in bone age assessment than Neural Network Regression (NNR).
  • SVR-based bone age assessment results closely matched evaluations by skilled radiologists.

Conclusions:

  • The developed edge-following method enhances carpal bone boundary extraction for automated systems.
  • Support Vector Regression (SVR) is a more accurate method for pediatric bone age assessment using carpal X-rays.
  • This technique shows promise for improving the efficiency and accuracy of clinical bone age determination.