Abnormal epithelial stratification is a sign of oral dysplasia and hence evaluation of surface characteristics of oral epithelial region can help in detection of cancerous progression. Surface characteristics can be better visualised by Scanning Electron Microscopy (SEM) in comparison to light microscopy. In our study we have developed automated image processing algorithms i.e. Gaussian with median filtering and Gradient filtering, using MATLAB 2016b, to segment the surface characteristics i.e. the ridges and pits in the SEM images of oral tissue of normal (13 samples) and Oral Submucous Fibrosis (OSF) (36 samples) subjects. After segmentation, quantitative measurement of the parameters like area, thickness and textural features like entropy, contrast and range filter of ridges as well as area of pit and the ratio of area of ridge vs. area of pit was done. Statistical significant differences were obtained in between normal and OSF study groups for thickness (p=0.0107), entropy (p<0.00001) and contrast of ridge (p<0.00001) for Gaussian with median filtering and for all the parameters except thickness of the ridge(p=1.386), for Gradient filtering. Thus, computer aided image processing by Gradient filter followed by quantitative measurement of the surface characteristics provided precise differentiation between normal and precancerous oral condition.

中文翻译：

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通过扫描电子显微镜获得的口腔组织表面特征的分割和分析，以区分正常和口腔癌前状态。

异常的上皮分层是口腔发育不良的征兆，因此评估口腔上皮区域的表面特征可以帮助检测癌症的进展。与光学显微镜相比，可以通过扫描电子显微镜（SEM）更好地观察表面特性。在我们的研究中，我们使用MATLAB 2016b开发了自动图像处理算法，即具有中值滤波和梯度滤波的高斯算法，以分割正常口腔（13个样本）和口腔粘膜下纤维化的口腔组织SEM图像中的表面特征（即脊和凹坑） （OSF）（36个样本）受试者。分割后，定量测量参数，如面积，厚度和纹理特征，如熵，脊的对比度和范围过滤器以及凹坑的面积以及脊与面积之比。坑的面积已完成。在正常和OSF研究组之间，对于具有中值滤波的高斯厚度和除脊线厚度以外的所有其他参数，其厚度（p = 0.0107），熵（p <0.00001）和脊线对比度（p <0.00001）获得了统计学上的显着差异（p = 1.386），用于渐变过滤。因此，通过梯度过滤器进行计算机辅助图像处理，然后对表面特征进行定量测量，可以准确区分正常口腔和癌前口腔状况。