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Femur segmentation in DXA imaging using a machine learning decision tree.

Dildar Hussain1, Mugahed A Al-Antari1, Mohammed A Al-Masni1

  • 1Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, Republic of Korea.

Journal of X-Ray Science and Technology
|July 30, 2018
PubMed
Summary
This summary is machine-generated.

A new Pixel Label Decision Tree (PLDT) method significantly improves femur segmentation in dual-energy X-ray absorptiometry (DXA) images, enhancing bone mineral density (BMD) accuracy for osteoporosis diagnosis.

Keywords:
Dual-energy X-ray absorptiometry (DXA)decision treefeature extractionfeature selectionmathematical morphologynon-local means filterosteoporosissegmentation

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Area of Science:

  • Medical Imaging
  • Radiology
  • Biomedical Engineering

Background:

  • Accurate bone mineral density (BMD) measurement using dual-energy X-ray absorptiometry (DXA) is crucial for osteoporosis diagnosis.
  • Precise femur segmentation in DXA images is challenging due to low contrast, noise, and variable X-ray attenuation.
  • Existing segmentation methods often fail to accurately distinguish femur from surrounding soft tissues.

Purpose of the Study:

  • To introduce and evaluate a novel Pixel Label Decision Tree (PLDT) method for enhanced femur segmentation in DXA imaging.
  • To assess the accuracy of PLDT compared to established segmentation algorithms.

Main Methods:

  • The Pixel Label Decision Tree (PLDT) method utilizes feature extraction and selection from high energy (HE) and low energy (LE) DXA images.
  • PLDT generates new feature maps to identify hidden patterns and selects optimal features for segmentation.
  • Performance was compared against Global Threshold (GT), Region Growing Threshold (RGT), and artificial neural networks (ANN) algorithms.

Main Results:

  • PLDT achieved a high accuracy of 91.4% for femur segmentation in DXA images.
  • This accuracy significantly surpasses that of GT (68.4%), RGT (76%), and ANN (84.4%).

Conclusions:

  • The Pixel Label Decision Tree (PLDT) method demonstrates superior performance over conventional techniques for DXA image segmentation.
  • Improved femur segmentation using PLDT is expected to enhance BMD calculation accuracy and aid in clinical osteoporosis diagnosis.