Quantitative Evaluation of Vocal-Fold Mucosal Irregularities Using GLCM-based Texture Analysis
- Quantitative Evaluation of Vocal-Fold Mucosal Irregularities Using GLCM-based Texture Analysis
- Chan Il Song; Chang Hwan Ryu; Seung-Ho Choi; Jong-Lyel Roh; Soon Yuhl Nam; 김상윤
- Laryngeal image; texture analysis; gray-level co-occurrence matrix; vocal fold
- Issue Date
- The Laryngoscope
- VOL 123, NO 11, E45-E50
Visualization of the vocal folds is essential when reaching a primary diagnosis of laryngeal disease. However, the examination is subjective and highly dependent on the experience of the treating physician. The present study is the development of objective tools for the diagnosis of laryngeal malignancy based on laryngeal texture analysis.
Texture analysis using gray-level co-occurrence matrix (GLCM) in vocal fold images of 198 patients.
Vocal-fold images were subjected to texture analysis using gray-level co-occurrence matrix (GLCM)-based parameters, which were computed by a novel digital image-processing program. Patients were divided into two groups: those with benign-looking lesions and those with malignant-looking lesions. Textural irregularities were compared using GLCM-based parameters. The relationship between the texture-analysis parameters and the diagnosis was then statistically evaluated.
Texture irregularity was negatively correlated with energy and the inverse difference moment (IDM) and positively correlated with entropy, variance, contrast, dissimilarity, and mean values. All of the GLCM-based parameters evaluated differed significantly according to the degree of differentiation of the benign- or malignant-looking lesion (P < 0.001). Entropy had a sensitivity of 82.9% and a specificity of 82.2% at a cutoff value of 5.94; for variance, the sensitivity was 82.9% and the specificity was 84.5% at a cutoff value of 167.
GLCM-based texture analysis of vocal-fold lesions, especially in association with a differential diagnosis of benign and malignant-looking diseases, contributes to achieving an objective image-based analysis of vocal-fold lesions. In addition, this approach can be used to create algorithms permitting a reproducible classification of laryngeal pathologies.
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