Department of Material Science and Engineering, National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Road, Da'an District, Taipei, Taiwan, ROC.
Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, No. 325, Sec. 2, Cheng-Gong Road, Neihu District, Taipei, 114, Taiwan, ROC.
Sci Rep. 2020 Jul 3;10(1):10975. doi: 10.1038/s41598-020-67587-1.
Laryngopharyngeal reflux (LPR) is a prevalent disease affecting a high proportion of patients seeking laryngology consultation. Diagnosis is made subjectively based on history, symptoms, and endoscopic assessment. The results depend on the examiner's interpretation of endoscopic images. There are still no consistent objective diagnostic methods. The aim of this study is to use image processing techniques to quantize the laryngeal variation caused by LPR, to judge and analyze its severity. This study proposed methods of screening sharp images automatically from laryngeal endoscopic images and using throat eigen structure for automatic region segmentation. The proposed image compensation improved the illumination problems from the use of laryngoscope lens. Fisher linear discriminant was used to find out features and classification performance while support vector machine was used as the classifier for judging LPR. Evaluation results were 97.16% accuracy, 98.11% sensitivity, and 3.77% false positive rate. To evaluate the severity, quantized data of the laryngeal variation was used. LPR images were combined with reflux symptom index score chart, and severity was graded using a neural network. The results indicated 96.08% accuracy. The experiment indicated that laryngeal variation induced by LPR could be quantized by using image processing techniques to assist in diagnosing and treating LPR.
喉咽反流（LPR）是一种常见疾病，影响到相当一部分寻求喉科咨询的患者。诊断主要基于病史、症状和内镜评估进行主观判断。结果取决于检查者对内镜图像的解释。目前仍然没有一致的客观诊断方法。本研究旨在使用图像处理技术对 LPR 引起的喉部变化进行量化，以判断和分析其严重程度。本研究提出了从喉内窥镜图像中自动筛选清晰图像的方法，并使用喉咙固有结构进行自动区域分割。所提出的图像补偿方法改善了使用喉镜镜头引起的照明问题。Fisher 线性判别用于寻找特征和分类性能，而支持向量机则用于判断 LPR。评估结果的准确率为 97.16%，敏感度为 98.11%，假阳性率为 3.77%。为了评估严重程度，使用了喉部变化的量化数据。将 LPR 图像与反流症状指数评分图表相结合，并使用神经网络对严重程度进行分级。结果表明准确率为 96.08%。实验表明，LPR 引起的喉部变化可以通过图像处理技术进行量化，以辅助诊断和治疗 LPR。
