臺灣博碩士論文加值系統

機能亢進病症是最常見的甲狀腺疾病，其中有70-80%的甲狀腺機能亢進患者屬於葛雷芙氏症（Graves’ Disease）。在臨床診斷上，醫師除了對病人進行超音波影像的檢測外，仍須對病人施行抽血檢驗，才能確認是否罹患葛雷芙氏症。然而，血液分析需耗時一周以上的時間，無法達到即時診斷的目的，對於急性的病患，醫師無法給予即時的用藥。而超音波影像的優點則有影像成本便宜、成像迅速以及無須藉由游離輻射成像等特性，使醫師可達到即時診斷的目的。因此本論文將提出一個自動葛雷芙氏症檢測的方法，輔助醫師在超音波影像上進行即時的診斷。本系統將先偵測出大略的甲狀腺區域，輔以支援向量機(Support Vector Machine, SVM)進行初步的分類，接著使用重疊區域累計方法，判斷可疑葛雷芙氏症區域，最後經由診斷分數決定是否為葛雷芙氏病症。實驗結果顯示，本論文所提出的方法在超音波影像上葛雷芙氏病症的診斷能獲得良好的正確率。

Hyperthyroidism is a common thyroid disease. Graves'' disease is one of the most common etiology with 70-80% of hyperthyroidism. In clinical diagnosis, physicians generally utilize US images for inspections. Unfortunately, they are not able to diagnose the Graves’ disease directly on the US images, and have to rely on blood tests to assure it. The blood tests often take weeks to obtain the inspection results. Hence, in this paper, we proposed an automatic approach to diagnose Graves’ disease on US image fast and directly. The automatic diagnosis meets a short time inspection, and patients may know their situation quickly. We segment the thyroid regions, and utilize a high performance classifier (SVM) to classify the regions. The diagnostic result is determined using a diagnosis score on the classified regions. Experimental results show effectiveness of the proposed approach.

目錄
摘要
ABSTRACT
誌謝
目錄
表格索引
圖例索引
第一章 緒論
1.1 甲狀腺歷史
1.2 研究動機
1.3 葛雷芙氏病症簡介
1.4 甲狀腺超音波影像簡介
1.5 研究方法
1.6 章節大綱
第二章 甲狀腺區域切割方法
2.1 可疑甲狀腺區域的定位和影像增強
2.1.1 可疑甲狀腺區域的定位
2.1.2 適應性權重中值濾波器
2.1.3 形態學運算子
2.1.4 灰階強度校正
2.2 甲狀腺切割特徵擷取
2.3 半徑基底函數類神經網路
2.3.1 RBF類神經網路訓練
2.3.2 RBF類神經網路測試
2.3.3 甲狀腺區域連結
2.4 甲狀腺區域形狀之恢復
2.4.1 移除邊緣非甲狀腺區塊區域
2.4.2 區域成長
2.4.3 區域填充與閉合形態學運算
2.4.4 濾除區域鋸齒
第三章 甲狀腺葛雷芙氏症診斷
3.1 判斷並取得可疑假影區域
3.1.1 取得可疑假影區域
3.1.2 判斷影像是否包含假影區域
3.2 假影區域補償和中點濾波器
3.3 中點濾波器
3.4 葛雷芙氏病症診斷特徵擷取
3.5 支援向量機
3.5.1 SVM類神經網路訓練
3.5.2 SVM類神經網路測試
3.6 重疊區域進行累計
3.7 葛雷芙氏病症診斷判斷
第四章 實驗結果與討論
4.1 影像資料與實驗環境
4.2 超音波影像中甲狀腺葛雷芙氏症之自動檢測參數評估
4.2.1 測試區塊大小及重疊率的評估
4.2.1.1 正確率為測試區塊大小及重疊率的評估
4.2.1.2 診斷面積為測試區塊大小及重疊率評估
4.2.2 條件比例評估
4.2.3 類神經網路評估
4.3 超音波影像中甲狀腺葛雷芙氏症之自動檢測方法探討
4.3.1 葛雷芙氏症診斷前處理探討
4.3.2 葛雷芙氏症診斷重疊區域方法探討
4.4 超音波影像中甲狀腺葛雷芙氏症之自動檢測結果
4.5 超音波影像中甲狀腺葛雷芙氏症之自動檢測方法使用介紹
第五章 結論與未來發展方向
5.1 結論
5.2 未來發展方向
參考文獻
Proc. Of HIS2009, Page 192~197,“Automatic Diagnosis of Thyroid Graves’ Disease in Ultrasound Images"

[1] 林宏達“認識甲狀腺”,文史哲出版社，1982.
[2] C.C. Chang, C.J. Lin, LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
[3] S.L. Schulz, U. Seeberger, J.H. Hengstmann, “Color Doppler sonography in hypothyroidism,” European Journal of Ultrasound, Vol. 16, pp. 183-189, 2003.
[4] N. Hu, D. B. Downey, A. Fenster, and H. M. Ladak, “Prostate boundary segmentation from 3d ultrasound images,” Medical Physics, vol. 30, no. 7, pp. 1648-1659, July 2003.
[5] B. Chiu, G. H. Freeman, M. M. A. Salama, and A. Fenster, “Prostate segmentation algorithm using dyadic wavelet transform and discrete dynamic contour,” Physics in Medicine and Biology, vol. 49, no. 21, pp. 4943-4960, Nov. 2004.
[6] D.-R. Chen, R.-F. Chang, W.-J. Wu, W. K. Moon, and W.-L. Wu, “3-D breast ultrasound segmentation using active contour model,” Ultrasound in Medicine and Biology, vol. 29, no. 7, pp. 1017-1026, July 2003.
[7] C. Baillard, C. Barillot, and P. Bouthemy, “Robust adaptive segmentation of 3d medical images with level sets,” Institut national de recherche en informatique et en automatique (INRIA), Le Chesnay Cedex, France, Tech. Rep. 4071, Nov. 2000.
[8] D.E. Maroulis, M.A. Savelonas, D.K. Iakovidis, S.A. Karkanis, and N. Dimitropoulos, “Variable Background Active Contour Model for Computer-Aided Delineation of Nodules in Thyroid Ultrasound Images”, IEEE Transactions on Information Technology in Biomedicine, vol. 11, no. 5, pp. 537-543, 2007.
[9] C.Y. Chang, Y.F. Lei, “Thyroid segmentation and volume estimation in Ultrasound images,” Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 3442-3447, 2008.
[10] T. Loupas, W.N. McDicken, and P.L. Allan, “An adaptive weighted median filter for speckle suppression in medical ultrasonic images,” IEEE Transactions on Circuits and Systems, vol. 36, pp. 129-135, Jan. 1989.
[11] Rafael C. Gonzalez, Richard E. Woods, “Digital Image Processing,” 2nd ed., Prentice-Hall International Edition, 2002.
[12] Y. D. Chum, S. Y. Seo, “Image Retrieval using BDIP and BVLC Moments,” IEEE Transactions on Circuits and Systems for Video Tech., vol. 13, no. 9, pp. 951-957, Sept. 2003.
[13] Chen EL, Chung PC, Chen CL, Tsai HM, Chang CI. “An automatic diagnostic system for CT liver image classification,” IEEE Transactions on Biomedical Engineering, vol. 45, no. 6, pp. 783-794, 1998.
[14] FM Ham and I. Kostanic, “Principles of Neurocomputing for Science and Engineering,” McGraw-Hill, New York, 2001.
[15] 張斐章, 張麗秋, “類神經網路, ” 東華書局, 民94年.
[16] C.Y. Chang, M.F. Tsai, and S.J. Chen, “Classification of the Thyroid Nodules Using Support Vector Machines,” Proc. of IEEE World Congress on Computational Intelligence, pp.3093-3098, 2008
[17] R.M. Haralick., K. Shanugam, and I. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man and Cybernetics, vol. 3, no, pp. 610-621, 1973.
[18] M.M. Galloway, “Texture Analysis Using Gray Run Lengths,” Computer Graphics and Image Processing, vol. 4, pp.172-179, 1975.
[19] K. Laws, “Textures image segmentation,” Ph.D. Dissertation, University of Southern California, 1980.
[20] S. Haykin, “Neural Networks,” Prentice-Hall Internation, 1999
[21] B.E. Boser, I.M. Guyon, V.N. Vapnik, “A training algorithm for optimal margin classifiers,” Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144-152, 1992.
[22] M. F. Akay, “Support vector machines combined with feature selection for breast cancer diagnosis,” Expert Systems with Applications, vol. 36, no. 1, pp. 3240-3247, 2009.
[23] C.C. Lai, C.Y. Chang, ”A hierarchical evolutionary algorithm for automatic medical image segmentation,” Expert Systems with Applications, vol. 36, no. 1, pp. 248-259, 2009.
[24] C.Y. Chang, M.F. Tsai, and S.J. Chen, “Classification of the Thyroid Nodules Using Support Vector Machines,” Proc. of IEEE World Congress on Computational Intelligence, pp.3093-3098, 2008