本研究「智慧型自動辨識鼻前庭及鼻中隔於電腦斷層影像之三維重建系統」係運用影像處理技術擷取電腦斷層影像訊號圖結合倒傳遞類神經網路(Back-propagation Network)自動擷取電腦斷層(Computed Tomography, CT)影像之鼻前庭(Nasal vestibule)及鼻中隔(Nasal septum)區域，並進行此區域三維影像重建以量測三維空間訊息。現今醫學診斷以手動方式圈選電腦斷層影像有欲參考區域，再由軟體將進行三維重建(Three dimensional reconstruction)，故本研究開發一套智慧型自動辨識鼻前庭及鼻中隔之三維重建系統，藉由影像處理技術結合倒傳遞類神經網路將鼻前庭及鼻中隔區域分割出，將每張電腦斷層影像分別標記，並利用三維重建技術進行鼻腔、鼻咽及鼻竇之三維影像，最後標記出腦部、眼內框及眼眶下緣三大潛在手術危險區域之代表點，進而量測鼻內資訊與標記點之距離，用於輔助醫師於手術前進行分析及疾病判斷之依據，以降低人為因素所造成之失誤。本研究所開發之智慧型自動辨識鼻前庭及鼻中隔系統其整體辨識率達99.7%，且三維影像經三軍總醫院耳鼻喉科醫師證明具有其參考價值，故本研究加快醫師於手術前之判斷參考依據，助於提升醫療品質。

This study entitled “Three-dimensional Reconstruction System of Intelligent Automatic Detection of Nasal Vestibule and Nasal Septum in Computed Tomography Images” attempted to combine the image processing technology to capture the computed tomography image signal and the back-propagation network for the automatic capturing of the nasal vestibule and nasal septum areas in computed tomography images. Moreover, it reconstructed the three-dimensional images by combining the two areas with skull and nose to measure the three-dimensional information. The present medical diagnosis often relies on computed tomography images to manually select the areas for reference, and use the software to conduct the three dimensional reconstruction measurement of the selected area for the reference of the pre-operational judgment. Therefore, this study developed a three-dimensional reconstruction system of intelligent automatic detection of nasal vestibule and nasal septum in computed tomography images. The proposed system employs the image processing technology combined with back-propagation network to segment the nasal vestibule and nasal septum areas, and mark each computed tomography image individually for the three dimensional reconstruction of the nasal vestibule and nasal septum areas. Finally, the representative points of three operational risky areas, including brain, eye rim internal side and the eye rim lower edge, were marked in order to measure the distance between intranasal information and marked points. The system could assist doctors in pre-operation analysis and judgment with more nasal information to reduce errors caused by human factors. The overall detection rate of the proposed three-dimensional measurement system of intelligent automatic detection of nasal vestibule and nasal septum in computed tomography images reached 99.7%. The three-dimensional image presentation combined with the skull and nose has been confirmed by doctors of the Department of Otolaryngology - Head and Neck Surgery, at Tri-Service General Hospital as valuable in reference. The study findings can facilitate the pre-operation diagnosis and judgment of doctors, as well as help to improve medical quality and the development of the medical industry.

[1] Wang, Z., Zeng, F., Li, H., Ye, Z., Bai, Y., Xia, W., and Liang, B., “Three-dimensional reconstruction on PC-Windows platform for evaluation of living donor nephrectomy Original Research Article,’’ Computer Methods and Programs in Biomedicine, Vol. 86, No. 1, pp. 39-44 (2007).
[2] Biedert, R., Sigg, A., Gal, I., and Gerber, H., “3D representation of the surface topography of normal and dysplastic trochlea using MRI,’’ The Knee, Vol. 18, No 5, pp. 340-346 (2011).
[3] Wang, R., Yang, D., and Li, S., “Three-dimensional virtual model and animation of penile lengthening surgery,” Journal of Plastic, Reconstructive & Aesthetic Surgery, Available online 2 May (2012).
[4] Annette, M., Pham, A. A., Rafii, M. C., Metzger, A. J., and Strong, E. B. “Computer modeling and intraoperative navigation in maxillofacial surgery,’’ Otolaryngology - Head and Neck Surgery, Vol. 137, No. 4, pp. 624-631 (2007).
[5] Guyomarc’h, P., Santos, F., Dutailly, B., Desbarats, P., Bou, C., and Coqueugniot, H., “Three-dimensional computer-assisted craniometrics: A comparison of the uncertainty in measurement induced by surface reconstruction performed by two computer programs,’’ Forensic Science International, Vol. 219, No. 1–3, pp. 221-227 (2012).
[6] Haralambidis, A., Ari-Demirkaya, A., Acar, A., Kucukkeleş, N., Ateş, M., and Ozkaya, S., “Morphologic changes of the nasal cavity induced by rapid maxillary expansion: A study on 3-dimensional computed tomography models,’’ American Journal of Orthodontics and Dentofacial Orthopedics, Vol. 136, No. 6, pp. 815-P.821 (2009).
[7] Gorgulu, S., Gokce, S. M., Olmez, Huseyin., and Sagdic, D., “Nasal cavity volume changes after rapid maxillary expansion in adolescents evaluated with 3-dimensional simulation and modeling programs,’’ American Journal of Orthodontics and Dentofacial Orthopedics, Vol. 140, No. 5, pp. 633-640 (2011).
[8] Matteo, N., Domenico, S., Luigi, M. C., Joaquim, E., Isam, A., Guadalupe, S., Joan, B. G., Enrique, F., and Alberto P. G., “The Use of a Three-Dimensional Novel Computer-Based Model for Analysis of the Endonasal Endoscopic Approach to the Midline Skull Base,’’ World Neurosurgery, Vol. 75, No. 1, pp.106-113 (2011).
[9] Zhu, J. H. Lee, H. P., Lim, K. M., Lee, S. J., and Wang, D. Y., “Evaluation and comparison of nasal airway flow patterns among three subjects from Caucasian, Chinese and Indian ethnic groups using computational fluid dynamics simulation,’’ Respiratory Physiology & Neurobiology, Vol. 175, No. 1, pp. 62-69 (2011).
[10]Otsu, N., “A Threshold Selection Method From Gray Level Histogram,” IEEE Trans. on Systems, Man, and Cybernetics, Vol. 9, No. 1, pp. 62-66 (1979).
[11]Johannsen, G. and Bille, J., “A Threshold Selection Method using Information Measures,” Proc. 6th Int. Conf. Pattern Recognition Munich, Germany, pp. 140-143 (1982).
[12]Kapur, J. N., Sahoo, P. K., and Wong, A. K. C., “A New Method for Gray-level Picture Thresholding using the Entropy of the Histogram,” Computer Vision, Graphics, and Image Processing, Vol. 29, No. 3, pp. 273-285 (1985).
[13]Tsai, W., “Moment-preserving Thresholding: A New Approach,” Computer Vision, Graphics, and Image Processing, Vol. 29, No. 3, pp. 377-393 (1984).
[14]張斌編著(1996)。《耳鼻喉科學》。中正書局。
[15]林滄雄譯者(1995)。《耳鼻喉疾病學》。合記圖書出版社。
[16]http://210.60.224.4/ct/content/1980/00030123/0005.htm
[17]陳名聖等人(2005)。《高價影像健檢比一比》。健康雜誌。
[18]李文胜(1997)。《CT機的物理原理》。工科物理。
[19]Frank, E., Hall, M. A., Holmes, G., Kirkby, R. B., Pfahringer, B., and Witten, I. H., Data Mining., “Knowledge Discovery Handbook,” Springer, pp. 1305-1314 (2005).
[20]http://www.mathworks.com/products/matlab/description1.html
[21]http://www.drabba.com/avizo/
[22]繆紹剛(2009)。《數位影像處理3/e》。培生教育出版集團。
[23]鍾國亮(2002)。《影像處理與電腦視覺》。東華書局。
[24]連國珍(2009)。《數位影像處理MATLAB》。儒林圖書公司。
[25]http://medical.nema.org/
[26]Lim, J. S., “Two-Dimensional Signal and Image Processing,”NJ, Prentice Hall, pp. 478-488 (1990).
[27]Parker, J. R., “Algorithms for Image Processing and Computer Vision, ” New York, John Wiley & Sons, Inc., pp. 23-29 (1997).
[28]John, C., “A Computational Approach to Edge Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, No. 6, pp. 679-698 (1986).
[29]Bouaynaya, N., “Theoretical Foundations of Spatially-variant Mathematical Morphology Part II：Gray-level Images,” IEEE Trans. on Pattern Analysis and Maxhine Intelligence, Vol. 30, No.5, pp. 831-850 (2008).
[30]Haralick, R. M., and Shapiro, L. G., “Addison-Wesley’’, Computer and Robot Vision, Vol. 1, No. 4, pp. 28-48 (1992).
[31]羅華強(2005)。《類神經網路MATLAB的應用》。高立圖書。
[32]Lorensen, W. E., and Cline, H. E., “Marching Cubes：A High Resolution 3D Surface Construction Algorithm”, Computer Graphics, Vol. 21, No. 4, pp. 163-169 (1987).