Abstract
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.
Highlights
Laryngoscopes are widely used in the clinical diagnosis of laryngeal lesions, but such diagnosis relies heavily on the physician’s subjective experience
By using hue and geometric analysis for the laryngeal region, automatic pathological change detection and classification were proposed for healthy vocal cords and laryngeal lesions, including vocal cord polyps, cysts, leukoplakia, and tumors
As it is difficult to take clear images by laryngoscopy, this study proposed searching for the clearest of the images taken by a laryngoscope, so as to solve the problem of difficulty of capturing clear images
Summary
Laryngoscopes are widely used in the clinical diagnosis of laryngeal lesions, but such diagnosis relies heavily on the physician’s subjective experience. 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. 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. About 90% of such abnormal sounds are caused by structural voice d isorder[1], including vocal cord polyps, cysts, leukoplakia, and tumors. To assist physicians in the correct diagnosis and appropriate treatment for patients, computer analysis of endoscopic images has been used in many studies to objectively diagnose laryngeal lesions. By using hue and geometric analysis for the laryngeal region, automatic pathological change detection and classification were proposed for healthy vocal cords and laryngeal lesions, including vocal cord polyps, cysts, leukoplakia, and tumors. The doctor could support his diagnosis with our system results
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