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Mandibular Trabecular Bone Analysis Using Local Binary Pattern for Osteoporosis Diagnosis.

L Khojastepour1, M Hasani1, M Ghasemi1

  • 1Department of Oral and Maxillofacial Radiology, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran.

Journal of Biomedical Physics & Engineering
|March 19, 2019
PubMed
Summary
This summary is machine-generated.

Panoramic X-rays can screen for osteoporosis by analyzing jaw trabecular patterns. This texture analysis method achieves high accuracy, offering a new tool for early osteoporosis detection.

Keywords:
Local Binary PatternPanoramicTexture AnalysisOsteoporosis

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Area of Science:

  • Radiology
  • Biomedical Imaging
  • Osteoporosis Research

Background:

  • Osteoporosis is a skeletal disease marked by low bone density and deterioration, increasing fracture risk.
  • Panoramic radiography, a common dental imaging technique, offers an opportunistic method for osteoporosis screening.
  • Jaw trabecular pattern analysis from radiographs is crucial for assessing bone quality and strength.

Purpose of the Study:

  • To investigate the correlation between jaw trabecular patterns in panoramic images and osteoporosis.
  • To utilize texture analysis with local binary patterns for osteoporosis assessment from dental radiographs.

Main Methods:

  • A dataset of panoramic radiographs from osteoporosis and non-osteoporosis patients was analyzed.
  • A modified local binary pattern approach was employed to extract features from jaw radiographs.
  • Various classifiers were used to diagnose osteoporosis based on extracted features.

Main Results:

  • The proposed method achieved a classification accuracy of approximately 99.6%.
  • This accuracy surpasses many existing state-of-the-art methods for osteoporosis detection.
  • The approach demonstrated high efficacy in distinguishing between osteoporosis and non-osteoporosis cases.

Conclusions:

  • The developed approach offers high classification accuracy for osteoporosis screening using panoramic images.
  • Advantages include low computational complexity, multi-resolution capability, and rotation invariance.
  • This method presents a promising, non-invasive tool for early osteoporosis detection.