臺灣博碩士論文加值系統

以肝臟電腦斷層掃描影像來判斷肝囊腫、肝凹狀血管瘤和肝癌等三種肝臟疾病時，雖是極富經驗的專業醫療人士也無法達到完全無誤的正確診斷率，為做進一步的確認，往往需要再對病人做侵入式的檢查才行。本實驗提出一電腦輔助肝臟疾病診斷系統，先將所欲研究的組織之影像區塊選取出來，並擷取出這些影像區塊的影像灰階值特徵、共現矩陣特徵和Gabor濾波器特徵等三種影像特徵。由於前述特徵所構成的特徵向量之維度相當高，所以我們使用PCA和SFS等降低向量維度的方法降低該特徵向量的維度，再利用SVM和RBFNN兩種人工智慧演算法做為分類器對降低維度前後的特徵向量做出分類，最後再以ROC曲線的分析方法來評估分類器的分類效能，以期能強化電腦輔助肝臟疾病診斷系統的判斷能力。

Traditionally, diagnosis of liver disease such as liver cyst, cavernous hemangioma and hepatoma is heavily dependent on professional radiologists. However, it is not guaranteed to make a highly accurate decision even for a specialist with a lot of experience. We propose a scheme of computer-aided diagnosis system which aims to assist radiologists in making more precise diagnosis of liver diseases. First of all, we select the region of interests (ROIs) from images with appropriate size. Secondly, the features including gray-level, co-occurrence matrix, and a bank of Gabor filters are extracted from the selected ROIs. Then both the methods of principle component analysis (PCA) and sequential forward selection (SFS) are used to reduce the dimension of feature vectors. The reduced features are fed into classifiers in which support vector machine (SVM) and radial basis neural network (RBFNN) are employed as the techniques for the classifiers. Finally, the analysis of receiver operating characteristic (ROC) curve is implemented to evaluate the performance of this system for the sake of getting higher distinction efficiency.

書名頁........................................................................................................................ii
論文口試委員審定書...............................................................................................iii
授權書.......................................................................................................................iv
中文摘要....................................................................................................................v
英文摘要...................................................................................................................vi
誌謝..........................................................................................................................vii
目錄.........................................................................................................................viii
圖目錄........................................................................................................................x
表目錄........................................................................................................................xi

第一章 緒論...............................................................................................................1
1.1 研究動機......................................................................................................1
1.2 研究目的......................................................................................................4
1.3 研究問題......................................................................................................5
1.4 文獻回顧......................................................................................................6
第二章 影像資料分析...............................................................................................9
2.1 電腦斷層掃描成像的演進..........................................................................9
2.2 肝臟疾病之CT影像分析..........................................................................10
第三章 研究方法.....................................................................................................13
3.1 肝臟組織影像區塊的選取........................................................................14
3.2 特徵擷取....................................................................................................16
3.2.1 影像灰階值特徵.................................................................................16
3.2.2 影像共現矩陣特徵.............................................................................19
3.2.3 Gabor濾波器對影像特徵的擷取.......................................................22
3.3 特徵向量之維度降低................................................................................25
3.3.1 主要成分分析.....................................................................................26
3.3.2 循序式前進選取.................................................................................27
3.4 分類器的使用............................................................................................27
3.4.1 SVM分類器.........................................................................................28
3.4.2 RBFNN分類器....................................................................................29
3.4.3 接收器運作指標曲線分析.................................................................30
第四章 實驗結果.....................................................................................................33
4.1 分類器效能分析－個別使用三種影像特徵...........................................35
4.2 分類器效能分析－合併使用三種影像特徵...........................................44
第五章 結論與展望.................................................................................................49
參考文獻...................................................................................................................51

[1]Bovik, Alan Conrad, Marianna Clark, and Wilson S. Geisler, “Multichannel texture analysis using localized spatial filters,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 1, pp. 55-73, Jan. 1990.
[2]Chen, E-Liang, Pau-Choo Chung, Ching-Liang Chen, Hong-Ming Tsai, and Chein-I Chang, “An automatic diagnostic system for CT liver image classification,” IEEE Trans. Biomedical Engineering, vol. 45, no. 6, pp.783-794 Jun. 1998.
[3]Chang, Chih-Chung and Chih-Jen Lin, LIBSVM : a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
[4]Clausi, David A., M. Ed Jernigan, “Designing Gabor filters for optimal texture separability,” Pattern Recognition 33 (2000), pp. 1835-1849.
[5]Clausi, David A. and Deng Huang, “Designed-based texture feature fusion using Gabor filters and co-occurrence probabilities,” IEEE Trans. Image processing, vol. 14, no. 7, pp. 925-936, Jul. 2005.
[6]Gosnik, B.B., S.K. Lemon, W. Scheible, and G. R. Leupold, “Accuracy of ultrasonography in diagnosis of hepatocellular disease,” AJR, vol. 133, pp. 19-23, 1979.
[7]Grigorescu, Simona E., Nicolai Petkov, and Peter Kruizinga, “Comparison of texture features based on Gabor filters,” IEEE Trans. Image Processing, vol. 11, no. 10, pp. 1160-1167, Oct. 2002.
[8]Gletsos, Miltiades, Stavroula G. Mougiakakou, George K. Matsopoulos, Konstantina S. Nikita, Alexandra S. Nikita, and Dimitrios Kelekis, “A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier,” IEEE Trans. Information technology in biomedicine, vol. 7, no. 3, pp.153-162, Sep. 2003.
[9]Huang, Jin and Charles X. Ling, “Using AUC and accuracy in evaluating learning algorithms,” IEEE Trans. Knowledge and Data Engineering, vol. 17, no. 3, pp. 299-310, Mar. 2005.
[10]Jain, Anil K., Robert P.W. Duin, and Jianchang Mao, “Statistical patern recognition: a review,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 4-37, Jan. 2000.
[11]Kruizinga, Peter, and Nikolay Petkov, “Nonlinear operator for oriented texture,” IEEE Trans. Image Processing, vol. 8, no. 10, pp. 1395-1407, Oct. 1999.
[12]Kim, Kwang In, Keechul Jung, Se Hyun Park, and Hang Joon Kim, “Support vector machines for texture classification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 11, pp. 1542-1550, Nov. 2002.
[13]Mitra, Pabitra, C.A. Murthy, and Sankar K. Pal, “Unsupervised feature selection using feature similarity,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 3, pp. 301-312, Mar. 2002.
[14]Pratt, William K., Digital Image Processing, Wiley, New York, 2001.
[15]Park, Seong Ho, Jin Mo God, and Chan-Hee, “Receiver operating characteristics (ROC) curve: practical review for radiologists,” Korean J Radiol, pp. 11-18, Mar. 2004.
[16]Sonka, Milan, Vaclav Hlavac and Roger Boyle, eds., Image Processing, Analysis and Machine Vision, Chapman and Hall, New York, 1993.
[17]Schölkopf, Bernhard, Kah-Kay Sung, Chris J.C. Burges, Federico Girosi, Partha Niyogi, Tomaso Poggio, and Vladimir, “Comparing support vector machine with Gaussian kernels to radial basis function classifiers,” IEEE Trans. Signal Processing, vol. 45, no. 11, pp. 2758-2765, Nov. 1997.
[18]Vapnik, V., The Nature of Statistical Learning Theory, Springer-Verlag, New York, 1995.
[19]Wei, Liyang, Yongyi Yang, Robert M. Nishikawa, and Yulei Jiang, “A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications,” IEEE Trans. Medical Imaging, vol. 24, no. 3, pp. 371-380, Mar. 2005