Scanning Electron Microscopic Texture Study of Periodontally Affected Teeth Using the Gray Level Co-occurrence Matrix A new Modeling Strategy for Diagnosing Periodontal Disease.

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1 Lecturer, Department of Oral Medicine, Periodontology, Oral Diagnosis and Oral Radiology, Faculty of dental medicine, Al-Azhar University, PO 11825, Cairo, Egypt

2 Lecturer of Oral Biology. Faculty of Dental Medicine, (Boys –Cairo), Al-Azhar University, Cairo, Egypt

Abstract

Introduction: Scanning electron microscopy provides a valuable method to investigate minor changes that could predispose to periodontal diseases. The goal of this study is to deeply investigate and analyze the surface texture of periodontal-affected teeth utilizing a gray-level co-occurrence matrix, and to provide a primary model for periodontal diagnosis. Methods: Eighty teeth were obtained from a dental clinic at the Faculty of Dental Medicine, Al-Azhar University, Cairo, Egypt. The teeth were extracted due to irreversible periodontal disease. The present study utilized 41 scanning electron microscopic images of obtained periodontal-affected teeth, processed them using a gray level co-occurrence matrix, and compared them to 14 images of healthy teeth extracted for orthodontic therapeutic purposes. Teeth with gross caries or fractures were excluded. The Whitney U test was used to compare the two groups. The significance level was set at P ≤ 0.05 using SPSS 28. Results: The textural characteristic analyzed utilizing the gray level co-occurrence matrix revealed that the periodontal-affected group had an insignificantly greater angular second moment, z = [ -1.408], p = [.159]. Correlation [z = [2.713], p = [.040] Inverse Difference Moment z = [-1.390], p = [.165], Entropy. z = [-1.408], p = [.159] The sum of all GLCM elements z = [ .000], p = [ 1.000] than normal group. Conclusion: The textural features analysis using gray level co-occurrence matrix could be a promising technique for surface changes evaluations that accompany and/or predispose to periodontal diseases. It also provides an avenue for image modelling for periodontal diagnosis.

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