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首页> 外文期刊>Journal of voice: official journal of the Voice Foundation >Detection of chronic laryngitis due to laryngopharyngeal reflux using color and texture analysis of laryngoscopic images
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Detection of chronic laryngitis due to laryngopharyngeal reflux using color and texture analysis of laryngoscopic images

机译：使用喉镜图像的颜色和纹理分析检测由于咽喉返流引起的慢性喉炎

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Objective To determine if pattern recognition of hue and textural parameters can be used to identify laryngopharyngeal reflux (LPR). Methods Laryngoscopic images from 20 subjects with LPR and 42 control subjects without LPR were obtained. LPR status was determined using the reflux finding score. Color and texture features were quantified using hue calculation and two-dimensional Gabor filtering. Five regions were analyzed: true vocal folds, false vocal folds, epiglottis, interarytenoid space, and arytenoid mucosae. A multilayer perceptron artificial neural network with varying numbers of hidden nodes was used to classify images according to pattern recognition. Receiver operating characteristic (ROC) analysis was used to evaluate diagnostic utility, and intraclass correlation coefficient analysis was performed to determine interrater reliability. Results Classification accuracy when including all parameters was 80.5% ± 1.2% with an area under the ROC curve of 0.887. Classification accuracy decreased when including only hue (73.1% ± 3.5%; area under the curve = 0.834) or texture (74.9% ± 3.6%; area under the curve = 0.852) parameters. Interrater reliability was 0.97 ± 0.03 for hue parameters and 0.85 ± 0.11 for texture parameters. Conclusions This preliminary study suggests that a combination of hue and texture features can be used to detect chronic laryngitis due to LPR. A simple, minimally invasive assessment would be a valuable addition to the currently invasive and somewhat unreliable methods currently used for diagnosis. Including more data will likely improve classification accuracy. Additional investigations will be performed to determine if results are in accordance with those provided by pH probe monitoring.
机译：目的确定色调和结构参数的模式识别是否可用于识别喉咽反流（LPR）。方法获得20例LPR患者和42例非LPR患者的喉镜图像。使用反流发现评分确定LPR状态。使用色调计算和二维Gabor过滤对颜色和纹理特征进行量化。分析了五个区域：真人声带，假人声带，会厌，ary间间隙和突粘膜。使用具有不同隐藏节点数的多层感知器人工神经网络，根据模式识别对图像进行分类。使用接收器工作特性（ROC）分析来评估诊断效用，并通过组内相关系数分析来确定区间可靠性。结果当包括所有参数时，分类精度为80.5％±1.2％，ROC曲线下的面积为0.887。仅包含色相（73.1％±3.5％;曲线下面积= 0.834）或纹理（74.9％±3.6％;曲线下面积= 0.852）参数时，分类准确性降低。色调参数的内部评估者可靠性为0.97±0.03，纹理参数为0.85±0.11。结论这项初步研究表明，结合色相和质地特征可用于检测LPR引起的慢性喉炎。一个简单的，微创的评估将是对当前用于诊断的当前侵入性和某种程度不可靠的方法的宝贵补充。包含更多数据可能会提高分类准确性。将进行其他调查以确定结果是否与pH探针监控所提供的结果一致。

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