Laryngopharyngeal reflux image quantization and analysis of its severity.
Sci Rep
; 10(1): 10975, 2020 07 03.
Article
em En
| MEDLINE
| ID: mdl-32620899
ABSTRACT
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Interpretação de Imagem Assistida por Computador
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Refluxo Laringofaríngeo
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Laringoscopia
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article