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Quantitative evaluation of vocal-fold mucosal irregularities using GLCM-based texture analysis.

Chan Il Song1, Chang Hwan Ryu, Seung-Ho Choi

  • 1Department of Otolaryngology, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, Korea.

The Laryngoscope
|May 21, 2013
PubMed
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Objective laryngeal texture analysis using gray-level co-occurrence matrix (GLCM) aids in diagnosing vocal fold lesions. This method provides reproducible classification of benign and malignant-appearing diseases.

Area of Science:

  • Otolaryngology
  • Medical Imaging
  • Computational Pathology

Background:

  • Laryngeal disease diagnosis relies on subjective vocal fold visualization.
  • Physician experience significantly impacts diagnostic accuracy.
  • Objective diagnostic tools for laryngeal malignancy are needed.

Purpose of the Study:

  • To develop objective tools for diagnosing laryngeal malignancy.
  • To utilize laryngeal texture analysis for objective diagnosis.
  • To differentiate benign from malignant vocal fold lesions using image analysis.

Main Methods:

  • Texture analysis of vocal fold images from 198 patients using gray-level co-occurrence matrix (GLCM).
  • A novel digital image-processing program computed GLCM-based parameters.
Keywords:
Laryngeal imagegray-level co-occurrence matrixtexture analysisvocal fold

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  • Statistical evaluation of texture parameters against benign and malignant lesion diagnoses.

    • Main Results:

    • Texture irregularity correlated with GLCM parameters like entropy and variance.
    • All GLCM parameters significantly differed between benign and malignant lesions (P < 0.001).
    • Entropy and variance showed high sensitivity and specificity for differentiating lesions.

    Conclusions:

    • GLCM-based texture analysis offers objective, image-based assessment of vocal fold lesions.
    • This approach aids in the differential diagnosis of benign and malignant laryngeal diseases.
    • The method can form the basis for reproducible laryngeal pathology classification algorithms.