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Analysis of periapical lesion using statistical textural features.

B Caputo1, G E Gigante

  • 1Physics Department, University of Rome La Sapienza Piazza A. Moro 3, 00161 Rome, Italy.

Studies in Health Technology and Informatics
|February 24, 2001
PubMed
Summary

This study uses textural features from periapical images and artificial neural networks to objectively identify changes in bone patterns. This quantitative analysis enables reliable detection of bone alterations in dental imaging.

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Area of Science:

  • Dental Imaging
  • Biomedical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Periapical radiography is crucial for diagnosing bone changes in dentistry.
  • Objective assessment of bone patterns in periapical images remains a challenge.

Purpose of the Study:

  • To quantitatively analyze textural features in periapical images.
  • To develop an objective method for recognizing bone pattern changes using artificial neural networks.

Main Methods:

  • Extraction of textural features using co-occurrence matrices from periapical image regions.
  • Pattern recognition analysis employing an artificial neural network.

Main Results:

  • Successfully extracted and utilized textural features for pattern recognition.

Related Experiment Videos

  • Demonstrated the objective recognition of bone pattern alterations.

    • Conclusions:

    • Artificial neural networks can objectively identify bone pattern changes in periapical images.
    • Textural feature analysis provides a quantitative approach to dental bone assessment.