本研究旨在開發一套智慧型聲帶疾病辨識系統，其應用影像處理技術結合類神經網路於聲帶影像疾病辨識，以輔助醫師對聲帶疾病進行判斷，進而降低人為所產生的誤判與主觀上面的認知。本研究辨識的聲帶症狀包括正常聲帶、聲帶麻痺、聲帶息肉及聲帶癌。首先先把原始影像轉換成灰階影像，接著應用影像強化中的柱狀圖平均法調整對比度，使影像有良好的對比度以後再以影像分割中的統計式門檻值分割前景與背景，接著對二值影像進行影像形態學中的標籤化與膨脹侵蝕的運算。最後得到聲門位置，再用三組實驗特徵值進行分析。
將三組的特徵值輸入至類神經網路，經過自行研究的辨識系統驗證95個樣本測試之後，得知第三組的特徵值效果最好，其總辨識率達93.6%。此實驗結果證明本研究發展出的智慧型聲帶疾病辨識系統有不錯的辨識能力，能即時輔助醫師在臨床上面的判斷，降低人為所發生的誤判與主觀上的認知，進而提升病患品質與工作效率。

The purpose of this study was to develop an intelligent recognition system for vocal cord disorders. The image processing technology and neural networks were applied to identify different disease conditions of vocal cords, including the presence of paralysis, tumor, malignant cancer, or no diseases. We first changed three random sets of original obtained images into a grayscale format, followed by applying histogram equalization to obtain a good contrast. We then used statistical threshold to segment the processed images. The binary images were lastly computed by labeling, dilation and erosion to obtain the position of the glottis.
The features of the three sets of images would be the input of the neural network. After testing 95 samples, the experimental results reveal that the third set had the best recognition rate reaching 93.6%. The results of this experiment support that the intelligent recognition system has the ability to identify vocal cord disorder. Thereby the problems obtained from misdiagnosis and subjective knowledge in the medical field could be reduced effectively.

1.Y. Yan, X. Chen, and D. Bless, “Automatic Tracing of Vocal-Fold Motion From High-Speed Digital Images,” IEEE Transactions on Biomedical Engineering, Vol. 53, No. 7, pp. 1394-1400, 2006.
2.A. Mendez, B. Garcia, I. Ruiz, and I. Iturricha, “Glottal Vocal-Fold without Initialization Using Gabor Filters,” Proceedings of the 19th Annualn International Conference of the IEEE EMBS Cite Internationale, Lyon, France May 23-26, pp. 18-22, 2008.
3.M. Bacauskiene, A. Verikas, and A. Gelzinis, “A Feature Selection Technique for Generation of Classification Committees and its Application to Categorization of Laryngeal Images,” Jounal of Pattern Recognition, Vol. 42, No. 12, pp. 645-654, 2009.
4.A. Mndez, B. Garca, J.Vicente, I. Ruiz, and K. Snchez, “Objective Model of Vocal Folds, Based on Glottal Closure,Opening and Morphologic Criteria,” Proceedings of the IEEE Engineering in Medicine and Biology, Vol. 53, No. 7, pp. 4244-4250, 2007.
5.M. Kudo and J. Sklansky, “Comparison of Algorithms that Select Features for Pattern Classifiers,” Jounal of Pattern Recognition, Vol. 33, No. 1, pp. 25-41, 2000.
6.A. Verikas, A. Gelzinis, and M. Bacauskiene, “Interating Global and Local Analysis of Colour,Texture and Geometrical Information for Categorizing Laryngeal Images,” Jounal of Pattern Recognition and artificial intelligence, Vol. 20, No. 8, pp. 1187-1205, 2006.
7.X. Chen, D. Bless, and Y. Yan, “A Segmentation Scheme Based on Rayleigh Distribution Model for Extracting Glottal Waveform from High-speed Laryngeal Images,” Proceedings of the IEEE Engineering in Medicine and Biology, Vol. 23, No. 6, pp. 6269-6272, 2005.
8.J. Wang and C. Jo, “Vocal Folds Disorder Detection Using Pattern Recognition Methods,” Proceedings of the 29th Annual International Conference of the IEEE EMBS Cit Internationale, Lyon, France, August 23-26, pp. 3253-3256, 2007.
9.Y. Yan, D. Bless, and X.Chen, “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.
10.S. Allin, J. Galeotti, and G. Stetten, “Enhanced Snake Based Segmentation of Vocal Folds,” Proceedings of the IEEE Engineering in Medicine and Biology, Vol. 53, No. 27, pp. 4125-4129, 2004.
11.R. Behroozmand and F. Almasganj, “Comparison of Neural Networks and Support Vector Machines Applied to Optimized Features Extracted from Patients’ Speech Signal for Classification of Vocal Fold Inflammation,” Proceedings of the IEEE International Symposium on Signal Processing and Information Technology, pp. 844-849, 2004.
12.江萬宣 編著，“耳鼻喉科醫學雜誌”，東華書局，1987。
13.謝地 編著，“耳鼻喉的疾病與治療”，全華科技書局，1995。
14.江萬宣 編著，“耳鼻喉及其疾病”，正中書局，1976。
15.張彬 編著，“耳鼻喉科學”，正中書局，1996。
16.李憲彥 編著， “耳鼻喉科醫學新知”，全華科技書局，1993。
17.R. C. Gonzalez and R. E. Woods, “Digital Image Processing,” 2nd ed., Prentice Hall, New York, 2002.
18.W. A. Bently, “The Inspection: An Automatic Optical Printed Circuit Board (PCB) Inspection,” Proceeding of the European Conference On Computer Vision, pp. 37-47, 1979.
19.A. Rosendeld and A. C. Kak, Digital Picture Processing, Vols. 1 and 2, 2nd ed., Academic Press, New York.
20.T. Chen, K. K. Ma, and L. H. Chen, “Tri-state Median Filter for Image Denoising,” IEEE Transactions on Image Processing, Vol. 8, No. 12, pp. 1834-1838, 1999.
21.S. J. Ko and Y. H. Lee, “Center Weighted Median Filters and Their Applications to Image Enhancement,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 38, No. 9, pp. 984-993, 1991.
22.繆紹綱 編著，“數位影像處理 活用-Matlab”，全華科技圖書，2001。
23.連國珍 編著，“數位影像處理”，第二版，儒林圖書公司，1999。
24.N. Otsu, “A Threshold Selection Method from Gray Level Histogram,” IEEE Transactions. on Systems, Man, and Cybernetics, SMC-8, pp. 62-66, 1978.
25.C. C. Chen, J. S Daponte, and M. D. Fox,“Fractal Feature Analysis and Classification in Medical Imaging,” IEEE Transactions. on Med.Imaging, Vol. 8, No. 2, pp. 133-142, 1989.
26.L. Breaux and B. Singh, “Automatic Defect Classification System for Patterned Semiconductor Wafers,” Proceedings of the International Conference of Computer Vision, pp. 68-73, 2003.
27.T. Kiyan and T. Yildirim, “Brest Cancer Dlagnosis Using Statistical Neural Networks,” Journal of Electrical and Electronics Engineering, Vol. 4, No. 2 , pp. 1149-1153, 2004.
28.N. M. Salleh, H. A. M. Sakim, and N. H Othman, “Neural Networks to Evaluate Morphological Features for Breast Cells Classification,” International Journal of Computer Science and Network Security, Vol 8 , No. 9, pp. 51-58, 2008.
29.A. Verikas and M. Bacauskiene, “Feature Selection With Neural Networks,” Jounal of Pattern Recognition, Vol. 23, No. 11, pp. 1323-1335, 2002.