Surgical excision is an effective treatment for oral squamous cell carcinoma (OSCC), but exact intraoperative differentiation OSCC from the normal tissue is the first premise. As a noninvasive imaging technique, optical coherence tomography (OCT) has the nearly same resolution as the histopathological examination, whose images contain rich information to assist surgeons to make clinical decisions. We extracted kinds of texture features from OCT images obtained by a home-made swept-source OCT system in this paper, and studied the identification of OSCC based on different combinations of texture features and machine learning classifiers. It was demonstrated that different combinations had different accuracies, among which the combination of texture features, gray level co-occurrence matrix (GLCM), Laws’ texture measures (LM), and center symmetric auto-correlation (CSAC), and SVM as the classifier, had the optimal comprehensive identification effect, whose accuracy was 94.1%. It was proven that it is feasible to distinguish OSCC based on texture features in OCT images, and it has great potential in helping surgeons make rapid and accurate decisions in oral clinical practice.

中文翻译：

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基于纹理特征的光学相干断层扫描图像中口腔鳞状细胞癌的识别

手术切除是口腔鳞状细胞癌（OSCC）的有效治疗方法，但术中将OSCC与正常组织准确区分是首要前提。作为一种无创成像技术，光学相干断层扫描（OCT）具有与组织病理学检查几乎相同的分辨率，其图像包含丰富的信息，可帮助外科医生做出临床决策。本文从国产扫频OCT系统获得的OCT图像中提取各种纹理特征，研究了基于纹理特征和机器学习分类器的不同组合的OSCC识别。证明了不同的组合具有不同的精度，其中纹理特征的组合、灰度共生矩阵（GLCM）、劳斯纹理度量（LM）、中心对称自相关（CSAC）和SVM作为分类器综合识别效果最佳，准确率为94.1%。事实证明，基于OCT图像中的纹理特征区分OSCC是可行的，在帮助外科医生在口腔临床实践中做出快速准确的决策方面具有很大的潜力。