人體於用力呼吸時聲門張開至最大位置，而發音時聲門閉合至最小位置，因此對聲帶聲門做偵測時，擷取出聲門張開至最大位置與聲門閉合至最小位置以此為判斷依據。目前為止醫院仍採用人工方式挑選以喉閃頻內視鏡(Strobe-laryngoscope)攝影動態影像之聲門張開至最大位置與聲門閉合至最小位置之靜態影像，故自動化辨識動態影像輔助醫療系統為重要之需求。
本研究探討220組由喉閃頻內視鏡所攝影動態影像與30組加入雷射投標記模組所攝影動態影像，利用色彩空間之轉換與影像前處理自動篩選出聲門張開至最大位置與聲門閉合至最小位置之靜態影像，並量化聲門面積。由於所拍攝之影像並非固定不動，舌跟運動，內視鏡晃動以及唾液會造成非清晰之影像，因此本研究提出以灰階適應性熵值設定閥值，建立淘汰制度提升自動擷取聲門張開至最大位置與聲門閉合至最小位置影像之效果，其準確率可達96%。此外以各種影像前處理有效地計算聲門面積與區域分割閥值，準確聲門區域分割，自動化繪製聲門週期波形(Glottis Area Waveform, GAW)圖，以此為聲帶健康輔助依據。
於智慧型聲帶疾病辨識系統方面，本研究針對之聲帶分別為正常聲帶(Normal)、聲帶麻痺(Vocal Fold Paralysis)、聲帶息肉(Vocal Fold Polyp)及聲帶囊腫(Vocal Fold Cyst)四種不同類型之疾病，求取其特徵值且應用支持向量機( Support Vector Machine, SVM) 分類器做為辨識聲帶疾病之相關研究，辨識準確率可達98.75%。其結果可提供為醫師診斷病患病情之參考。

Human glottis opens to the largest extent when individuals breathe with maximal effort and closes to the smallest extent when individuals make voice. Therefore, the images of largest extent and the smallest extent of glottis are captured during video laryngoscope as the basis of identification of possible vocal fold lesion when the glottis is being examined. At present, doctors still select the static images of glottis opening to the largest extent and closing to the smallest extent in video clip recorded by strobe-laryngoscope manually, so the automatic recognition of dynamic image aided medical system is an important demand.
This study analyzed 220 groups of dynamic images photographed by strobe-laryngoscope and 30 groups of dynamic images photographed with additional laser marking module. The static images of glottis opening to the largest extent and closing to the smallest extent were filtered out automatically by using color space conversion and image preprocessing and the glottal area was quantized. As the tongue base movement affected the position of laryngoscope and saliva would result in unclear images, this study puts forward setting the threshold by using gray level adaptive entropy value, setting up an elimination system to improve the effect of automatically captured images of glottis opening to the largest extent and closing to the smallest extent. The accuracy rate can be 96%. In addition, the glottal area and region segmentation threshold were calculated effectively by using various image preprocessing. The vocal area segmentation was corrected, and the glottis area waveform (GAW) pattern was drawn automatically to assist in vocal fold health.
In the aspect of intelligent recognition system for vocal folds disorders, this study aims at four kinds of vocal folds such as normal vocal fold, vocal fold paralysis, vocal fold polyp and vocal fold cyst. By analyzing the characteristic eigenvalues of these four vocal folds patterns and the support vector machine (SVM) classifier to identify vocal folds disorders, the identification accuracy rate can be 98.75%. The results can be provided as assistance for doctors to diagnose the patient's conditions.

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