Combining Image Processing Techniques with Decision Tree Theory to Study the Vocal Fold Diseases Identification System

• Main Authors: Po-chun Wang, 王泊鈞
• Other Authors: Chung-feng Kuo
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/31203969053163705830

碩士 === 國立臺灣科技大學 === 自動化及控制研究所 === 100 === Larynx is the main breathing channel and vocal mechanism. Clinically, otolaryngologists use strobo-laryngoscopes to observe the movements of vocal fold and diagnose vocal fold disorders. As the current diagnostic method is to select images on the computer screen manually, this study attempted to design a set of automatic vocal fold diseases identification system. Using the films taken by doctors as the samples for experimental analysis, this study used image processing techniques to capture the images of the vocal fold opening to the maximum position and closing to the minimum position in order to replace the manual image selection process and enhance diagnostic efficiency. As the filming process may involve human factors that cause blurred images and non-vocal fold image, this study included texture analysis to measure the image smoothness and entropy, in order to develop a set of selection and elimination system that can effectively enhance the accuracy of the capture images. Moreover, for the images of the vocal fold opening to the maximum position, image processing was used to automatically analyze the glottis images and vibration position of the vocal fold, in order to obtain physiological parameters and plot the mucosa fluctuation diagram as the references for vocal fold health promotion. The vocal fold diseases identification system can be used to obtain the physiological parameters for normal, vocal paralysis, and vocal nodules. Decision tree method was used as a classier to categorize the vocal fold diseases. The identification accuracy was proven to be 92.6%, and it could be improved to 98.7% after combining image processing. Finally, the study measures texture feature and establishes a statistic table in the area of lesions between vocal cancer and vocal polyp. This system can serve as a reference for clinical use.