本研究擬開發一套利用電腦斷層影像準確計算肺癌病患接受化學治療後腫瘤體積之變化，做為輔助醫師臨床治療評估之參考，本研究主要分為兩部分，第一部分利用影像處理技術分析肺癌電腦斷層影像，分別定位腫瘤區域及計算腫瘤體積，首先經由韋納濾波與直方圖等化去除電腦斷層拍攝時所造成的斑點雜訊及加強影像對比，並設計肺部擷取流程，以權重模糊C均値進行肺部影像二値化，搭配形態學方法求得肺部區域遮罩，藉此定位腫瘤區域，並以綠框在原影像上標示出，達成腫瘤定位；接著將第一張腫瘤影像進行區域成長，並計算其中心做為下張影像之種子點，利用區域成長之邊界輪廓做為基於區域特徵等位函數法(active contour without edges, ACWE)之初始輪廓，ACWE不以影像梯度定義邊界，對於具有凹陷、缺口及模糊等邊緣其提取結果較佳，適用於肺癌腫瘤邊緣之偵測，最後利用等值面提取的Marching cube演算法三維重建腫瘤影像及計算其體積，並以形狀不規則之石頭驗證所求體積之可信度，其誤差為1.918%。
第二部分為醫學指標分析，比較三軍總醫院99位病患接受化學治療後腫瘤體積變化與存活期之關係，並經由Kaplan-Meier方法、對數等級檢定(log-rank test)及接收者操作特徵曲線(receiver operat-ing characteristic curve, ROC curve)，三統計方法分別求得1年、1.5年、2年、2.5年與3年之存活預測指標，與化學治療對於性別及年齡分佈之影響，提供臨床醫學於肺癌預後存活期與診斷有一新穎的參考指標。

This study plans to develop a reference system which uses computed tomography to calculate the tumor volume change of lung cancer patients after chemotherapy accurately to assist doctors in clinical treatment and evaluation. The study is divided into two parts. Part 1 uses image processing techniques to analyze the computed tomography of lung cancer, locate the tumor area and calculate the tumor volume. First, the speckle noise caused by computed tomography is removed and the image contrast is enhanced by wiener filter and histogram equalization, and the lung extracting process is designed, the weighted fuzzy-C means is used for lung image binarization, combined with morphological approach to obtain the lung area mask, so as to locate the tumor area, which is marked with a green frame on the original image for tumor localizing. Afterwards, the first tumor image is used for region growing, the center is used as the seed of next image, taking the boundary contour of the region growing as the initial contour for the active contour without edges (ACWE) algorithm. The ACWE does not use image gradient to define the boundary, it extracts the edges with seg, gap and blur better, applicable to the detection of lung tumor edge. Finally, the Marching cube algorithm of isosurface extraction is used for 3D reconstruction of tumor image and calculating the volume, and the accuracy is validated.
Part 2 is medical indicator analysis, the relationships between tumor volume change and survival time of 99 patients after chemotherapy in Tri-Service General Hospital are compared, and the 1-year, 1.5-year, 2-year, 2.5-year and 3-year novel survival prediction indicators and the effect of chemotherapy on sex and age distributions are obtained by three statistical methods, which are Kaplan-Meier method, log-rank test and receiver operating characteristic curve, providing the doctors with the reference for prognostic survival time and diagnosis of lung cancer.

[1]Oliveira, F., Tavares, J., “Medical image registration: a review,” Computer Methods in Biomechanics and Biomedical Engineering, Vol. 17, No. 2, pp. 79-93, 2014.
[2]Sotiras, A., Davatzikos, C., Paragios, N., “Deformable medical image registration: a survey,” IEEE Transactions on Medical Im-aging, Vol. 32, No. 7, pp. 1153-1190, 2013.
[3]衛生福利部，「中華民國103年版衛生福利年報」，臺灣台北，2014。
[4]Landis, H., Murray, T., Bolden, S., Wingo, S., “Cancer statistic,” CA: A Cancer Journal for Clinicians, Vol. 49, No. 1, pp. 8-31, 1999.
[5]Novello, S., Le Chevalier, T., “Chemotherapy for non-small-cell lung cancer. Part 1: early-stage disease,” Oncology, Vol. 17, No. 3, pp. 357-364, 2003.
[6]Waller, D., Peake, M., Stephens, R., Gower, N. H., Milroy, R., Parmar, M., Spiro, S., “Chemotherapy for patients with non-small cell lung cancer: the surgical setting of the big lung trial,” European Journal of Cardio-Thoracic Surgery, Vol. 26, No. 1, pp. 173-182, 2004.
[7]Sawyers, C., “Targeted cancer therapy,” Nature, Vol. 432, No. 7015, pp. 294-297, 2004.
[8]Albain, K., Swann, R., Rusch, V., Turrisi, A., Shepherd, F., Smith, C., Cox, J., “Radiotherapy plus chemotherapy with or without surgical resection for stage III non-small-cell lung cancer: a phase III randomised controlled trial,” The Lancet, Vol. 374, No. 9687, pp. 379-386, 2009.
[9]World Health Organization, “WHO handbook for reporting results of cancer treatment,” World Health Organization, No. 48, 1979.
[10]Eisenhauer, E., Therasse, P., Bogaerts, J., Schwartz, L. H., Sargent, D., Ford, R., Verweij, J., “New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1),” European Journal of Cancer, Vol. 45, No. 2, pp. 228-247, 2009.
[11]Therasse, P., Eisenhauer, E. A., Verweij, J., “RECIST revisited: a review of validation studies on tumour assessment,” European Journal of Cancer, Vol. 42, No. 8, pp. 1031-1039, 2006.
[12]Greenwood, J., Maredia, N., Younger, J., Brown, J., Nixon, J., Everett, C., Plein, S., “Cardiovascular magnetic resonance and single-photon emission computed tomography for diagnosis of coronary heart disease (CE-MARC): a prospective trial,” The Lancet, Vol. 379, No. 9814, pp. 453-460, 2012.
[13]Noble, J., “Ultrasound image segmentation and tissue characteri-zation,” Proceedings of The Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, Vol. 224, No. 2, pp. 307-316, 2010.
[14]Wen, H., Bennett, E., Hegedus, M., Rapacchi, S., “Fourier x-ray scattering radiography yields bone structural information 1,” Ra-diology, Vol. 251, No. 3, pp. 910-918, 2009.
[15]Furman, H. E., Feinberg, M. S., Badikhi, D., Eyal, E., Zehavi, T., Degani, H., “Standardization of radiological evaluation of dynamic contrast enhanced MRI: application in breast cancer diagnosis,” Technology in Cancer Research and Treatment, Vol. 13, No. 5, pp. 445-454, 2014.
[16]Prasad, V., Ambrosini, V., Hommann, M., Hoersch, D., Fanti, S., Baum, R., “Detection of unknown primary neuroendocrine tu-mours (CUP-NET) using 68Ga-DOTA-NOC receptor PET/CT,” European Journal of Nuclear Medicine and Molecular Imaging, Vol. 37, No. 1, pp. 67-77, 2010.
[17]Sohaib, S., Turner, B., Hanson, J., Farquharson, M., Oliver, R., Reznek, R., “CT assessment of tumour response to treatment: comparison of linear, cross-sectional and volumetric measures of tumour size,” The British Journal of Radiology, Vol. 73, No. 875, pp. 1178-1184, 2000.
[18]Mazumdar, M., Smith, A., Schwartz, L., “A statistical simulation study finds discordance between WHO criteria and RECIST guideline,” Journal of Clinical Epidemiology, Vol. 57, No. 4, pp. 358-365, 2004.
[19]Tran, L., Matthew, M., Goldin, J., “Comparison of treatment re-sponse classifications between unidimensional, bidimensional, and volumetric measurements of metastatic lung lesions on chest CT,” Academic Radiology, Vol. 11, No. 12, pp. 1355-1360, 2004.
[20]Marten, K., Auer, F., Schmidt, S., Kohl, G., Rummeny, E. J., Engelke, C., “Inadequacy of manual measurements compared to automated CT volumetry in assessment of treatment response of pulmonary metastases using RECIST criteria,” European Radi-ology, Vol. 16, No. 4, pp. 781-790, 2006.
[21]Mozley, P., Schwartz, L., Bendtsen, C., Zhao, B., Petrick, N., Buckler, A., “Change in lung tumor volume as a biomarker of treatment response: a critical review of the evidence,” Annals of Oncology, Vol. 21, No. 9, pp. 1751-1755, 2010.
[22]Zhang, J., Huang, Y., Li, X., Guo, Y., Zhao, Y., Xue, C., Hu, Z., Zhang, L., Zhao, H., “The impact of tumor size change after target therapy on survival: analysis of patients enrolled onto three clin-ical trials of advanced NSCLC from one institution,” Onco Targets and Therapy, Vol. 5, pp. 349-355, 2012.
[23]Ball, D., Fisher, R., Burmeister, B., Poulsen, M., Graham, P., Penniment, M., Vinod, S., Krawitz, H., Joseph, D., Wheeler, G., McClure, B., “The complex relationship between lung tumor volume and survival in patients with non-small cell lung cancer treated by definitive radiotherapy: a prospective, observational prognostic factor study of the Trans-Tasman radiation oncology group (TROG 99.05),” Radiotherapy and Oncology, Vol. 106, No. 3, pp. 305-311, 2013.
[24]Cai, W., Chen, S., Zhang, D., “Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation,” Pattern Recognition, Vol. 40, No. 3, pp. 825-838, 2007.
[25]Forgy, C., “Rete: a fast algorithm for the many pattern/many object pattern match problem,” Artificial Intelligence, Vol. 19, No. 1, pp. 17-37, 1982.
[26]Bezdek, J. C., Ehrlich, R., Full, W., “FCM: the fuzzy c-means clustering algorithm,” Computers and Geosciences, Vol. 10, No. 2, pp. 191-203, 1984.
[27]Ghosh, S., Dubey, S., “Comparative analysis of k-means and fuzzy c-means algorithms,” International Journal of Advanced Computer Science and Applications, Vol. 4, No. 4, pp. 35-38, 2013.
[28]Iyer, N., Kandel, A., Schneider, M., “Feature-based fuzzy classifi-cation for interpretation of mammograms,” Fuzzy Sets and Sys-tems, Vol. 114, No. 2, pp. 271-280, 2000.
[29]Boykov, Y., Olga, V., Ramin, Z., “Fast approximate energy mini-mization via graph cuts,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 11, pp. 1222-1239, 2001.
[30]Ferahta, N., Moussaoui, A., Benmahammed, K., Chan, V., “New fuzzy clustering algorithm applied to RMN image segmentation,” International Journal of Soft Computing, Vol. 1, No. 2, pp. 137-142, 2006.
[31]Xu, Z., Chen, J., Wu, J., “Clustering algorithm for intuitionistic fuzzy sets,” Information Sciences, Vol. 178, No. 19, pp. 3775-3790, 2008.
[32]Sivakumar, S., Chandrasekar, C., “Lungs image segmentation through weighted FCM,” Recent Advances in Computing and Software Systems (RACSS), 2012 International Conference on IEEE, Chennai, India, pp. 109-113, April 25-27, 2012.
[33]Kass, M., Witkin, A., Terzopoulos, D., “Snakes: active contour models,” International Journal of Computer Vision, Vol. 1, No. 4, pp. 321-331, 1988.
[34]Xu, C., Prince, J., “Snakes, shapes, and gradient vector flow,” IEEE Transactions on Image Processing, Vol. 7, No. 3, pp. 359-369, 1998.
[35]Osher, S., Sethian, J., “Fronts propagating with curvature de-pendent speed: algorithms based on the Hamilton-Jacobi formu-lation,” Journal of Computational Physics, Vol. 118, No. 2, pp. 269-277, 1995.
[36]Li, B., Chui, C., Chang, S., Ong, S., “Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation,” Computers in Biology and Medicine, Vol. 41, No. 1, pp. 1-10, 2011.
[37]Li, C., Xu, C., Gui, C., Fox, M., “Distance regularized level set evolution and its application to image segmentation,” IEEE Transactions on Image Processing, Vol. 19, No. 12, pp. 3243-3254, 2010.
[38]Mumford, D., Shah, J., “Optimal approximation by piecewise smooth functions and associated variational problems,” Commu-nication on Pure and Applied Mathematics, Vol. 42, No. 5, pp. 577-685, 1989.
[39]Chan, T., Vese, L., “Active contours without edges,” IEEE Trans-ac¬tions on Image Processing, Vol. 10, No. 2, pp. 266-277, 2001.
[40]鄭寅、王冰飛，「醫學影像圖像處理」，清華大學出版社，中國北京，2012。
[41]BioQuick News, http://www.bioquicknews.com/node/331, Feb 28, 2011.
[42]Travis, W., “Classification of lung cancer,” Seminars in Roentgen-ology, Vol. 46, No. 3, pp. 178-186, 2011.
[43]Nishino, M., Dahlberg, S., Cardarella, S., Jackman, D., Rabin, M., Hatabu, H., Johnson, B., “Tumor volume decrease at 8 weeks is associated with longer survival in EGFR-mutant advanced non-small-cell lung cancer patients treated with EGFR tyrosine kinase inhibitor,” Journal of Thoracic Oncology: Official Publica-tion of The International Association for The Study of Lung Can-cer, Vol. 8, No. 8, pp. 1059-1068, 2013.
[44]Janne, P., Gurubhagavatula, S., Yeap, B., Lucca, J., Ostler, P., Skarin, A., Johnson, B., “Outcomes of patients with advanced non-small cell lung cancer treated with gefitinib (ZD1839,‘Iressa’) on an expanded access study,” Lung Cancer, Vol. 44, No. 2, pp. 221-230, 2004.
[45]Goldstraw, P., Crowley, J., Chansky, K., Giroux, D. J., Groome, P. A., Rami-Porta, R., “The IASLC lung cancer staging project: proposals for the revision of the TNM stage groupings in the forthcoming (seventh) edition of the TNM classification of malig-nant tumours,” Journal of Thoracic Oncology, Vol. 2, No. 8, pp. 706-714, 2007.
[46]Li, C., Kao, C., Gore, J., Ding, Z., “Minimization of region-scalable fitting energy for image segmentation,” IEEE Transactions on Image Processing, Vol. 17, No. 10, pp. 1940-1949, 2008.
[47]Lorensen, W. E., Cline, H. E., “Marching cubes: a high resolution 3D surface construction algorithm,” Computer Graphics (ACM), Vol. 21, No. 4, pp. 163-169, 1987.
[48]Bland, J., Altman, D., “Survival probabilities (the Kaplan-Meier method),” British Medical Journal, Vol. 317, No. 7172, pp. 1572-1580, 1998.
[49]Schoenfeld, D., “The asymptotic properties of nonparametric tests for comparing survival distributions,” Biometrika, Vol. 68, No, 1, pp. 316-319, 1981.
[50]Hanley, J., McNeil, B., “The meaning and use of the area under a receiver operating characteristic (ROC) curve,” Radiology, Vol. 143, No. 1, pp. 29-36, 1982.
[51]Singh, S., Soni, M., Mishra, R., “Segmentation of underwater ob-jects using CLAHE enhancement and thresholding with 3-class fuzzy c-means clustering,” International Journal of Emerging Technology and Advanced Engineering, Vol. 4, No. 4, pp. 798-805, 2014.
[52]Radhika, V., Padmavathy, G., “Segmentation of oil spills SAR image using fusion technique,” International Journal of Advanced Engineering Sciences and Technologies, Vol. 10, No. 1, pp. 154-159, 2011.
[53]Wu, Y. C., Hsu, H. H., Chang, W. C., Tung, H. J., Ko, K. H., Hsu, Y. C., Huang, T. W., Ho, C. L., Chang, H., “Prognostic potential of initial CT changes for progression-free survival in gefitinib-treated patients with advanced adenocarcinoma of the lung: a preliminary analysis,” European Radiology, Vol. 25, No. 6, pp. 1801-1813, 2015.
[54]Youden, W. J., “Index for rating diagnostic tests,” Cancer, Vol. 3, No. 1, pp. 32-35, 1950.
[55]洪維恩，「Matlab7程式設計」，旗標出版股份有限公司，臺灣台北，2005。
[56]廖紹綱，「數位影像處理(digital image processing 3/e)」，臺灣培生教育出版股份有限公司，臺灣台北，2009。