簡易檢索 / 詳目顯示

回結果列表
研究生： 蕭尚妏
Shang-wun Hsiao
論文名稱： 雷射投影標記模組輔助喉閃頻內視鏡於動態聲門量測之智慧型影像辨識系統
The Intelligent Image Recognition System for Measuring Physiological Parameters of Glottis by using Strobo-Laryngoscope with Laser Projection Marking Module
指導教授： 郭中豐
Chung-Feng Jeffrey Kuo
口試委員: 王興萬
Hsing-Won Wang
黃昌群
Chang-Chiun Huang
高志遠
Chih-Yuan Kao
學位類別： 碩士
Master
系所名稱： 工程學院 - 材料科學與工程系
Department of Materials Science and Engineering
論文出版年： 2010
畢業學年度： 98
語文別： 中文
論文頁數： 103
中文關鍵詞： 喉閃頻內視鏡 、聲門生理參數 、數位影像處理 、雷射投影標記模組 、聲門面積
外文關鍵詞： strobo-laryngoscope, glottal physiological parameter, digital image processing, laser projection device, glottal area
相關次數： 點閱：230  下載：1 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 喉閃頻內視鏡(strobo-laryngoscope)為臨床上專業醫師使用檢驗聲門疾病(diseases of the glottis)的輔助儀器，並對聲帶影像與聲音品質判讀做出適當的診斷，由於每個人的聲帶位置不同，聲帶呈現於聲帶影像的大小比例也不同，導致無法直接估計，如聲帶長度(vocal length)、面積(glottal area)、周長(glottal perimeter)、開口角度(glottal angle)等聲門生理參數(glottal physiological parameter)。因此本研究設計雷射投影標記模組(laser projection device)架構於喉閃頻內視鏡上，提供影像尺度轉換參考參數，並配合以數位影像處理(digital image processing)技術，自動分離診斷上所需要辨識的重要區域，並使觀察聲帶影像的同時，又能從影像觀察的資料中得到聲門生理參數，協助醫師參考評估、減少醫療資源的消耗與提升醫師診斷效率。本研究並成功收集各20名正常男性及女性的聲帶樣本，統計國人正常聲門生理參數，可用於日後聲門疾病的觀察依據比較，能幫助手術前後的判斷，降低人為誤判與主觀上的認知，對於聽覺及發聲的臨床運用與研究也將有所助益。

    Strobo-Laryngoscope is a useful and assistant instrument for clinical diagnosis of vocal folds. Professional physicians interpret vocal folds imaging and make the diagnosis appropriate for the imaging. However, measuring vocal folds dimensions such as length, area, perimeter and angle, etc. in absolute quantities has been difficult, because the distance from the larynx to the tip of the endoscopy is varied. Therefore, there is no standard reference for calibrating endoscopic images. This study deals with the optical effect of endoscopy, the laser projection marking module is designed and integrated on endoscopy to provide a reference for scale transformation. Furthermore, this paper presents the digital image process technique to recognize vocal folds from endoscopy images, carried out on the extraction of objective parameters, which help to express a diagnosis more intuition and robust than the directly observation of images. An effort can assist physicians in assessing imaging, help to reduce the medical resources consumed and improve the efficiency of diagnosis. Finally, this study applied to 40 healthy voice subjects and established database of vocal parameters based on laser projection marking module and image process. This database enables accurate quantitative morphological measurement within the vocal folds that could be used even in clinical routine and helps to reduce the subjective judgments and opinions, and enhance the quality of medical treatment.

    目錄 摘要 1 ABSTRACT 2 誌謝 3 目錄 6 圖目錄 11 表目錄 14 第1章 緒論 15 1.1 研究動機 15 1.2 文獻回顧 16 1.2.1 聲帶檢測與疾病探討 17 1.2.2 聲帶檢測與影像處理 18 1.2.3 聲帶檢測與聲門面積 23 1.3 研究目的 24 1.4 論文架構 25 第2章 聲帶構造與功能疾病 28 2.1 聲帶相關構造 28 2.1.1 喉部軟骨 28 2.1.2 喉部結構 30 2.1.3 韌帶與彈性膜 31 2.1.4 喉部肌肉 31 2.1.5 神經系統 33 2.2 發聲功能 34 2.3 聲帶疾病與喉閃頻內視鏡 35 2.3.1 聲帶疾病 35 2.3.2 聲帶檢測 38 第3章 數位影像處理 40 3.1 影像擷取 40 3.2 影像前級處理 41 3.2.1 灰階轉換 41 3.2.2 影像負片 42 3.2.3 數位影像遮罩 43 3.2.4 直方圖等化法 44 3.2.5 空間濾波 46 3.2.5.1 中央加權中值法 47 3.2.6 銳化空間濾波器 48 3.2.6.1 索貝爾運算 48 3.3 影像分割 49 3.3.1 統計式門檻值決定法 49 3.4 形態學處理 52 3.4.1 標記化 52 3.4.2 膨脹 53 3.4.3 侵蝕 54 3.4.4 斷開和閉合 55 3.4.5 區域填充 56 3.5 影像特徵值 57 3.5.1 面積與周長 58 3.5.2 質心與角度 58 第4章 實驗與驗證 59 4.1 光學系統 59 4.1.1 幾何光學 59 4.1.2 物理光學 62 4.1.3 光度學 63 4.2 硬體架構 64 4.2.1 分光鏡 64 4.2.2 雷射 65 4.2.3 設計前置作業 65 4.2.4 雷射投影標記模組 68 4.2.4.1 雷射固定裝置及分光裝置 68 4.2.4.2 雷射導引裝置 69 4.2.4.3 原型製作 69 4.3 影像辨識軟體開發 72 4.4 實驗流程 73 4.4.1 雷射投影標計模組驗證 74 4.4.2 影像擷取方法流程 76 4.4.3 影像處理流程介紹 78 第5章 結果與討論 88 5.1 雷射投影標記模組 88 5.2 聲門區域數位影像處理 91 5.3 聲門生理參數轉換 92 5.4 聲門生理參數統計資料建立 93 第6章 結論與未來展望 96 6.1 結論 96 6.2 未來研究方向 97 參考文獻 98 作者簡介 103 圖目錄 圖 1 1論文架構流程圖 26 圖 2 1喉前部的軟骨構造[25] 28 圖 2 2喉後部的軟骨構造[25] 29 圖 2 3喉部的解剖結構[26] 30 圖 2 4喉部韌帶的構造[25] 31 圖 2 5喉外肌結構[25] 32 圖 2 6喉內肌結構[25] 32 圖 2 7喉部右側的神經系統分佈[25] 33 圖 2 8喉部相關疾病 36 圖 3 1彩色影像轉換為負片影像 43 圖 3 2數位影像遮罩 44 圖 3 3直方圖等化圖示 44 圖 3 4 3X3空間濾波遮罩 47 圖 3 5使用中央加權中值法去除雜訊 48 圖 3 6索貝爾運算子遮罩 49 圖 3 7二區的例子 50 圖 3 8標記化 53 圖 3 9膨脹處理結果 54 圖 3 10侵蝕處理結果 55 圖 3 11斷開處理結果 55 圖 3 12閉合處理結果 56 圖 3 13區域填充處理結果 57 圖 3 14三角形的餘弦定理 58 圖 4 1平面上的反射和折射現象示意圖 61 圖 4 2明視覺函數圖[41] 63 圖 4 3喉閃頻內視鏡平面圖 66 圖 4 4喉閃頻內視鏡模型 67 圖 4 5喉閃頻內視鏡標準測試架 68 圖 4 6雷射投影標記模組流程圖 71 圖 4 7雷射投影標記模組完成平面3D圖 71 圖 4 8雷射投影標記模組工件實體圖 72 圖 4 9程式操作介面 73 圖 4 10雷射與分光鏡反射關係示意圖 74 圖 4 11雷射光學實驗流程圖 75 圖 4 12雷射光束投影出的兩點距離 76 圖 4 13擷取聲帶影像實驗示意圖 77 圖 4 14雷射標記影像驗證 79 圖 4 15影像參數示意圖 80 圖 4 16影像處理實驗流程圖 81 圖 4 17聲帶原始影像 82 圖 4 18灰階影像 82 圖 4 19影像遮罩 83 圖 4 20雷射光束點以統計式門檻值決定法 83 圖 4 21雷射光束點以形態學處理過程 84 圖 4 22雷射光束點以形態學之二值化 84 圖 4 23雷射光束點以標記化 85 圖 4 24經由中央加權中值法與直方圖等化法之聲門圖 85 圖 4 25經由統計式門檻值理論之聲門圖 86 圖 4 26聲門以形態學處理過程 86 圖 4 27經由形態學處理之二值化圖 87 圖 5 1 雷射裝置與分光裝置3D立體圖 89 圖 5 2雷射導引裝置3D立體圖 89 表目錄 表 4 1依顏色細分可見光波長 62 表 4 2雷射簡介 65 表 4 3新台幣壹圓硬幣的影像面積與實際面積之比較表 79 表 5 1正常男女之聲門生理參數平均值 93 表 5 2男性之聲門生理參數統計數據 94 表 5 3女性之聲門生理參數統計數據 95

    參考文獻
    1. Eysholdt, U., Rosanowski, F., Hoppe, U., “Vocal Fold Vibration Irregularities Caused by Different Types of Laryngeal Asymmetry”, European Archives of Oto-Rhino-Laryngology, Vol. 260, No. 8, pp. 412-417 (2003).
    2. Michaelis, D., Gramss, T., Strube, H. W., “Glottal-To-Noise Excitation Ratio-A New Measure for Describing Pathological Voices”, Acta Acustica united with Acustica, Vol. 83, No. 4, pp. 700-706 (1997).
    3. Charles, S. C., Stacey, L. V., Emily, K. F., Marc, A. J., and Lucinda, A. H., “Sarcoidosis Presenting as Bilateral Vocal Cord Paralysis From Bilateral Compression of the Recurrent Laryngeal Nerves From Thoracic Adenopathy”, The Voice Foundation, Vol. 23, No. 5, pp. 631-634 (2009).
    4. Takano, S., Kimura, M., Takaharu, N., Imagawa, H., Sakakibara, K.-I., and Tayama, N., “Clinical Analysis of Presbylarynx-Vocal Fold Atrophy in Elderly Individuals”, Auris Nasus Larynx, Vol. 37, No. 4, pp. 461-464 (2010).
    5. Rahul, K. S., Henry, A. F., and Roger, C. N., “A Grading Scale For Pediatric Vocal Fold Nodules”, Otolaryngology-Head and Neck Surgery, Vol. 136, No. 2, pp. 193-197 (2007).
    6. Sipp, J. A., Kerschner, J. E., Braune, N., Hartnick, C. J., “Vocal Fold Medialization in Children”, Archives of Otolaryngology-Head & Neck Surgery, Vol. 133, No. 8, pp. 767-771 (2007).
    7. Schade, G., Leuwer, R., Kraas, M., Rassow, B., Hess, M. M., “Laryngeal Morphometry with a New Laser ‘Clip On’ Device”, Lasers in Surgery and Medicine, Vol. 34, No. 5, pp. 363-367 (2004).
    8. Yan, Y., Chen, X., and Bless, D., “Automatic Tracing of Vocal-Fold Motion From High-Speed Digital Images”, IEEE Transactions on Biomedical Engineering, Vol. 53, No. 7, pp. 1394-1400 (2006).
    9. Yumoto, E., Sanuki, T., Hyodo, M., “Three-Dimensional Endoscopic Images of Vocal Fold Paralysis by Computed Tomography”, Archives of Otolaryngology Head & Neck Surgery, Vol. 125, No. 8, pp. 883-890 (1999).
    10. Bresch, E., Narayanan, S., “Region Segmentation in the Frequency Domain Applied to Upper Airway Real-Time Magnetic Resonance Images”, IEEE Transactions on Medical Imaging, Vol. 28, No. 3, pp. 323-338 (2009).
    11. Marendicl, B., Galatsanos, N., and Bless, D., “A New Active Contour Algorithm For Tracking Vibrating Vocal Folds,” International Conference on Image Processing, Thessaloniki, Vol. 1, PP. 397-400 (2001).
    12. Allin, S., Galeotti, J., Stetten, G., Dailey, S. H., “Enhanced Snake Based Segmentation of Vocal Folds,” International Symposium on Biomedical Imaging: Mano to Macro, Vol. 1, PP. 812-815 (2004).
    13. Mendez, A., Garcia, B., Ruiz, I., Iturricha, I., “Glottal Area Segmentation Without Initialization Using Gabor Filters,” International Conference on Signal Processing and Information Technology, Sarajevo, PP. 18-22 (2008).
    14. Skalski, A., Zielinski, T., “Analysis of Vocal Folds Movement in High Speed Videoendoscopy Based on Level Set Segmentation and Image Registration,” International Conference on Signals And Electronic Systems, Krakow, PP. 223-226 (2008).
    15. Tao, C., Zhang, Y., and Jiang, J. J., “Extracting Physiologically Relevant Parameters of Vocal Folds From High-Speed Video Image Series”, IEEE Transactions on Biomedical Engineering, Vol. 54, No. 5, pp. 794-801 (2007).
    16. Verdonck-de Leeuw, I. M., Festen, J. M., and Mahieu, H. F., “Deviant Vocal Fold Vibration as Observed During Videokymography: The Effect on Voice Quality”, Journal of Voice, Vol. 15, No. 3, pp. 313-322 (2001).
    17. Tigges, M., Wittenberg, T., Mergell, P., Eysholdt, U., “Imaging of Vocal Fold Vibration by Digital Multi-plane Kymography”, Computerized Medical Imaging and Graphics, Vol. 23, No. 6, pp. 323-330 (1999).
    18. Qin, X., Wang, S., and Wan, M., “Improving Reliability and Accuracy of Vibration Parameters of Vocal Folds Based on High-Speed Video and Electroglottography”, IEEE Transactions on Biomedical Engineering, Vol. 56, No. 6, pp. 1744-1754 (2009).
    19. Lohscheller, J., Eysholdt, U., Toy, H., and Dollinger, M., “Phonovibrography：Mapping High-Speed Movies of Vocal Fold Vibrations Into 2-D Diagrams for Visualizing and Analyzing the Underlying Laryngeal Dynamics”, IEEE Transactions on Medical Imaging, Vol. 27, No. 3, pp. 300-309 (2008).
    20. Yan, Y., Bless, D., and Chen, X., “Biomedical Image Analysis in High-Speed Laryngeal Imaging of Voice Production”, Proceedings of the IEEE Engineering in Medicine and Biology, Vol. 53, No. 27, pp. 7684-7687 (2005).
    21. Osma-Ruiz, V., Godino-Llorente, J. I., Saenz-Lechon, N., Fraile, R., “Segmentation of the Glottal Space From Laryngeal Images Using the Watershed Transform”, Computerized Medical Imaging and Graphics, Vol. 32, No. 3, pp. 193-201 (2008).
    22. Verikas, A., Gelzinis, A., Bacauskiene, M., Valincius, D., Uloza, V., “A Kernel-Based Approach to Categorizing Laryngeal Images”, Computerized Medical Imaging and Graphics, Vol. 31, No. 8, pp. 587-594 (2007).
    23. Wang, X., Yu, X., Zhang, Y., Xu, X., “Automatic Detection of Vocal Folds from High-Speed Imaging,” International Conference on Image Analysis and Signal Processing, Zhejiang, China, PP. 199-201 (2010).
    24. Sharma, R. K., Raj, N., “MOS Modelling and Simulation of Human Glottis,” International Conference on Signal Acquisition and Processing, Bangalore, PP. 31-35 (2010).
    25. Fried, M. P., Ferlito, A., The Larynx, Plural Publishing, OX, pp. 85-99 (2009).
    26. Ossoff, R. H., Shapshay, S. M., Woodson, G. E., Netterville, J. L., The Larynx, Lippincott Williams & Wilkins, PA, pp. 33-51 & 338-377 (2003).
    27. Kocak, I., Aslan, G., Dogan, M., Comunoglu, N., “Vocal Fold Bridge：A Complication of a Sulcus Cyst Surgery”, Journal of Voice, Vol. 24, No. 2, pp. 240-241 (2010).
    28. Klein, A. M., Lehmann, M., Hapner, E. R., and Johns, M. M., “Spontaneous Resolution of Hemorrhagic Polyps of The True Vocal Fold”, Journal of Voice, Vol. 23, No. 1, pp. 132–135 (2009).
    29. Bergamini, G., Alicandri-Ciufelli, M., Molteni, G., Villari, D., Luppi, M. P., Genovese, E., Presutti, L., “Therapy of Unilateral Vocal Fold Paralysis With Polydimethylsiloxane Injection Laryngoplasty: Our Experience”, Journal of Voice, Vol. 24, No. 1, pp. 119-125 (2010).
    30. Verikas, A., Gelzinis, A., Bacauskiene, M., Valincius, D., Uloza, V., “A Kernel-Based Approach to Categorizing Laryngeal Images”, Computerized Medical Imaging and Graphics, Vol. 31, No. 8, pp. 587–594 (2007).
    31. Wu, X., “YIQ Vector Quantization in a New Color Palette Architecture”, IEEE Transactions on Image Processing, Vol. 5, No. 2, pp. 321-329 (1996).
    32. Yeganeh, H., Ziaei, A., Rezaie, A., “A Novel Approach for Contrast Enhancement Based on Histogram Equalization,” Proceedings of the International Conference on Computer and Communication Engineering, Kuala Lumpur, Malaysia, pp. 256-260.
    33. Sauter, D., and Parson, L., “Spatial Filtering for Speckle Reduction, Contrast Enhancement, and Texture Analysis of GLORIA Images”, IEEE Journal of Oceanic Engineering, Vol. 19, No. 4, pp. 563-576 (1994).
    34. Ko, S. J., and Lee, Y. H., “Center Weighted Median Filters and Their Applications to Image Enhancement”, IEEE Transactions on Circuits and Systems, Vol. 38, No. 9, pp. 984-993 (1991).
    35. Brownigg, D. R. K., “The Weighted Median Filter”, Communications of the ACM, Vol. 27, No. 8, pp. 807-818 (1984).
    36. Otsu, N., “A Tlreshold Selection Method from Gray-Level Histograms”, IEEE Transactions on Systrems, Man and Cybernetics, Vol. 9, No. 1, pp. 62-66 (1979).
    37. Liao, P. S., Chen, T. S., and Chung, P. C., “A Fast Algorithm for Multilevel Thresholding”, Journal of Information Science and Engineering, Vol. 17, No. 5, pp. 713-727 (2001).
    38. Haralick, R. M., Sternberg, S. R., and Zhuang, X., “Image Analysis Using Mathematical Morphology”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 9, No. 4, pp. 532-550 (1987).
    39. F. Chang and C. J. Chen, “A Component-Labeling Algorithm Using Contour Tracing Technique,” Proceedings of 7th International Conference on Document Analysis and Recognition, pp. 741-745 (2003).
    40. Haralick, R. M., Shanmugam, K., and Dinstein, I., “Textural Features for Image Classification”, IEEE Transactions Systems Man and Cybernetics, Vol. 3, No. 6, pp. 610-621 (1973).
    41. Pedrotti, F. L., Pedrotti, L. S., Pedrotti, L. M., Introduction to Optics, Pearson Prentice Hall, NJ, pp. 16-20 & 421-424 (2007).
    42. Al-Azzawi, A., Physical Optics : Principles and Practices, CRC Press, NW, pp. 7-8 (2007).
    43. Katz, M., Introduction to Geometrical Optics, World Scientific, MA, pp.1-2 (2002).
    44. Smith, F. G., King, T. A., Wilkins, D., Optics and Photonics : An Introduction, John Wiley & Sons, PO, pp. 1-3 (2007).
    45. Hudec, R., Marsikova, V., Mika, M., Sik, J., Lorenc, M., Pina, L., Inneman, A., and Skulinova, M., “Advanced X-ray Optics with Si Wafers and Slumped Glass,” Proceedings of SPIE, San Diego, CA, USA, Vol. 7437, PP. 0S1-OS12 (2009).
    46. Zimmer, F., Niklaus, F., Lapisa, M., Ludewig, T., Bring, M., Friedrichs, M., Bakke, T., Schenk, H., and Wijngaart, W., “Fabrication of Large-Scale Mono-Crystalline Silicon Micro-Mirror Arrays Using Adhesive Wafer Transfer Bonding,” Proceedings of SPIE, San Jose, CA, USA, Vol. 7208, PP. 071-079 (2009).

    無法下載圖示 全文公開日期 2015/07/31 (校內網路)
    全文公開日期 本全文未授權公開 (校外網路)
    全文公開日期 本全文未授權公開 (國家圖書館：臺灣博碩士論文系統)
    QR CODE