Department of Materials Science and Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, Republic of China.
Department of Otolaryngology-Head and Neck Surgery, Taichung Armed Forces General Hospital, Taichung, Taiwan, Republic of China.
Sci Rep. 2021 May 12;11(1):10147. doi: 10.1038/s41598-021-89680-9.
Laryngoscopes are widely used in the clinical diagnosis of laryngeal lesions, but such diagnosis relies heavily on the physician's subjective experience. The purpose of this study was to develop a computer-aided diagnostic system for the detection of laryngeal lesions based on objective criteria. This study used the distinct features of the image contour to find the clearest image in the laryngoscopic video. First to reduce the illumination problem caused by the laryngoscope lens, which could not fix the position of the light source, this study proposed image compensation to provide the image with a consistent brightness range for better performance. Second, we also proposed a method to automatically screen clear images from laryngoscopic film. Third, we used ACM to segment automatically them based on structural features of the pharynx and larynx, using hue and geometric analysis in the vocal cords and other zones. Finally, the support vector machine was used to classify laryngeal lesions based on a decision tree. This study evaluated the performance of the proposed system by assessing the laryngeal images of 284 patients. The accuracy of the detection for vocal cord polyps, cysts, leukoplakia, tumors, and healthy vocal cords were 93.15%, 95.16%, 100%, 96.42%, and 100%, respectively. The cross-validation accuracy for the five classes were 93.1%, 94.95%, 99.4%, 96.01% and 100%, respectively, and the average test accuracy for the laryngeal lesions was 93.33%. Our results showed that it was feasible to take the hue and geometric features of the larynx as signs to identify laryngeal lesions and that they could effectively assist physicians in diagnosing laryngeal lesions.
喉镜广泛应用于喉部病变的临床诊断，但这种诊断严重依赖于医生的主观经验。本研究旨在开发一种基于客观标准的计算机辅助诊断系统，用于检测喉部病变。本研究利用图像轮廓的明显特征，在喉镜视频中找到最清晰的图像。首先，为了解决喉镜镜头无法固定光源位置所导致的照明问题，本研究提出了图像补偿，为图像提供一致的亮度范围，以获得更好的性能。其次，我们还提出了一种从喉镜胶片中自动筛选清晰图像的方法。第三，我们使用 ACM 基于咽和喉的结构特征，在声带和其他区域使用色调和几何分析，自动对它们进行分割。最后，使用支持向量机基于决策树对喉部病变进行分类。本研究通过评估 284 名患者的喉部图像来评估所提出系统的性能。对声带息肉、囊肿、白斑、肿瘤和健康声带的检测准确率分别为 93.15%、95.16%、100%、96.42%和 100%。五类的交叉验证准确率分别为 93.1%、94.95%、99.4%、96.01%和 100%，平均喉部病变测试准确率为 93.33%。我们的结果表明，以喉的色调和几何特征作为标志来识别喉部病变是可行的，并且可以有效地帮助医生诊断喉部病变。
