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）是一种普遍的疾病，影响到寻求喉镜咨询的大部分患者。诊断根据病史，症状和内窥镜评估进行主观判断。结果取决于检查者对内窥镜图像的解释。仍然没有一致的客观诊断方法。这项研究的目的是使用图像处理技术来量化由LPR引起的喉部变异，以判断和分析其严重性。这项研究提出了从喉镜内窥镜图像中自动筛选出清晰图像并使用喉部特征结构进行自动区域分割的方法。所提出的图像补偿改善了由于使用喉镜而引起的照明问题。使用Fisher线性判别器找出特征和分类性能，同时使用支持向量机作为LPR的分类器。评价结果的准确性为97.16％，灵敏度为98.11％，假阳性率为3.77％。为了评估严重程度，使用了喉部变异的量化数据。LPR图像与反流症状指数评分表相结合，并使用神经网络对严重程度进行分级。结果表明96.08％的准确性。实验表明，LPR诱发的喉部变异可以通过图像处理技术来量化，以协助诊断和治疗LPR。使用喉部变异的量化数据。LPR图像与反流症状指数评分表相结合，并使用神经网络对严重程度进行分级。结果表明96.08％的准确性。实验表明，LPR诱发的喉部变异可以通过图像处理技术来量化，以协助诊断和治疗LPR。使用喉部变异的量化数据。LPR图像与反流症状指数评分表相结合，并使用神经网络对严重程度进行分级。结果表明96.08％的准确性。实验表明，LPR诱发的喉部变异可通过图像处理技术进行量化，以协助LPR的诊断和治疗。