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

Journal: Journal of X-ray science and technology
Published Date: Jan 1, 2018

Abstract

BACKGROUND: Accurate measurement of bone mineral density (BMD) in dual-energy X-ray absorptiometry (DXA) is essential for proper diagnosis of osteoporosis. Calculation of BMD requires precise bone segmentation and subtraction of soft tissue absorption. Femur segmentation remains a challenge as many existing methods fail to correctly distinguish femur from soft tissue. Reasons for this failure include low contrast and noise in DXA images, bone shape variability, and inconsistent X-ray beam penetration and attenuation, which cause shadowing effects and person-to-person variation.

Authors

  • Dildar Hussain
    Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, Republic of Korea.
  • Mugahed A Al-Antari
    Department of Computer Science and Engineering, College of Software, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104 Republic of Korea.
  • Mohammed A Al-Masni
    Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, Republic of Korea.
  • Seung-Moo Han
    Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, Republic of Korea.
  • Tae-Seong Kim
    Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, Republic of Korea.

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