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Classification of certain vertebral degenerations using MRI image features.

Jiyo S Athertya1, G Saravana Kumar2

  • 1Department of Engineering Design, IIT - Madras, Chennai-600036, Tamil Nadu, India.

Biomedical Physics & Engineering Express
|May 13, 2021
PubMed
Summary

This study introduces an automated system using machine learning to classify spinal degenerations like Modic changes from MRI scans, aiding in lower back pain diagnosis. The system accurately identifies vertebral abnormalities, assisting radiologists in treatment planning.

Keywords:
automatic classificationendplate defectsfeature detectionmodic changesvertebral degenerations

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Spinal Diagnostics

Background:

  • Lower back pain is a common ailment with diverse anatomical and pathophysiological causes.
  • Magnetic Resonance Imaging (MRI) is crucial for assessing spinal damage and guiding treatment.
  • Manual analysis of MRI scans is time-consuming in large healthcare systems.

Purpose of the Study:

  • To develop a fully automated system for classifying spinal degenerative phenotypes associated with lower back pain.
  • To enable automated detection of Modic changes, endplate defects, and focal changes from MRI scans.
  • To assist radiologists in efficient abnormality detection and treatment planning.

Main Methods:

  • Implementation of a system combining feature extraction, geometric image analysis, and machine learning classification.
  • Extraction of image features including Local Binary Pattern, Hu's moments, and Gray Level Co-occurrence Matrix (GLCM).
  • Utilizing a combined feature set for Modic change extent description and sensitivity analysis; STIR-based acute/chronic classification attempted.

Main Results:

  • The framework achieved 85.91% accuracy in detecting the extent of Modic changes.
  • Feature sensitivity analysis indicated that GLCM entropy alone is sufficient for focal change detection.
  • Endplate defect classification performance is highly sensitive to the first two Hu's moments.

Conclusions:

  • A novel machine learning approach effectively identifies vertebral degenerations and Modic changes using image features.
  • The automated system aids radiologists in detecting abnormalities and optimizing treatment strategies.
  • This technology offers a potential solution for streamlining spinal abnormality assessment in clinical practice.