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The study introduces a novel method to assess vertebral body deformation for osteoporosis detection using computed tomography data, leveraging an algorithm focused on analyzing geometric features. The algorithm's performance is demonstrated on real CT images, showing that certain features—such as compactness, shape, Haralick and Blair-Bliss texture metrics, and contour shape characteristics exhibit strong separability for distinguishing between healthy and osteoporotic vertebrae. This algorithm enhances the precision of osteoporosis diagnosis and significantly decreases the time needed for image evaluation.

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Algorithm for Evaluating the Geometric Features of Vertebral Bodies Using Computed Tomography Data for the Diagnosis of Osteoporosis

  • 会议论文

  • 发表日期：

    2025年

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  • Nataly Ilyasova,
  • Kamil Tadjitdinov,
  • Nikita Demin,
  • Valeriia Pavlova,
  • Eduard Alekhin

摘要

The study introduces a novel method to assess vertebral body deformation for osteoporosis detection using computed tomography data, leveraging an algorithm focused on analyzing geometric features. The algorithm's performance is demonstrated on real CT images, showing that certain features—such as compactness, shape, Haralick and Blair-Bliss texture metrics, and contour shape characteristics exhibit strong separability for distinguishing between healthy and osteoporotic vertebrae. This algorithm enhances the precision of osteoporosis diagnosis and significantly decreases the time needed for image evaluation.