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研究生:陳錫祺
研究生(外文):Hsi-Chi Chen
論文名稱:肝臟腫瘤切片影像的快速檢索機制之研究
論文名稱(外文):HCC Biopsy Images Indexing System in Liver Canner
指導教授:戴紹國戴紹國引用關係
指導教授(外文):Shou-Guo Dai
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:資訊管理系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:34
中文關鍵詞:肝臟病理切片相似性擷取支持向量機肝癌病理分級
外文關鍵詞:similarity retrievalliver biopsy imageSVMliver cancer grading
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肝癌不僅是全世界最常見的癌症之一,根據衛生署的統計,過去二十多年來,肝癌一直高居國內癌症死亡原因第一、二名。而肝臟中的腫瘤可分成惡性和良性。惡性腫瘤又包括很多種,如:肝癌、膽管癌、淋巴癌、惡性肉瘤…等;而良性的腫瘤也有很多種,如局部結節性增生、腺瘤、囊腫、血管瘤再生性結節…等。不同的狀況其治療方式與疾病預後會有相當大的差異,因此必須利用各種方式來做最正確的鑑別診斷。雖然超音波、X光和核磁共振等的檢測方法可以檢測出肝臟病變,然而肝臟的病理切片卻能提供肝癌最準確的資訊。而其中腫瘤細胞分級結果對於肝癌治療和預後尤其具有很大的影響。然而分級的標準是主觀的、非量化的,因此常會因主觀的認定而出現不一致的結果,尤其是在一些非典型分級的肝癌細胞更是容易有分歧的意見。
因此我們針對腫瘤病理切片的影像建立一個快速相似性比對機制,使醫師做診斷有必要時可以針對有疑慮的幾個切片影像進行以內容為基礎的類似性快速比對,自以往病例中搜尋出較類似的切片影像,檢視其診斷結果和相關診療過程作為參考,讓醫師可以更快速而正確的進行診斷。
Liver cancer is one of the most common cancers in the world. According to the report of the Department of Health, liver Cancer is still the most deathful disease in Taiwan. The tumor in liver can be either benignant or malignant. The malignant liver tumor includes hepatocellular carcinoma, bile duct cancer,lymphoma, maligmant sarcorma and so on. The benignant liver tumor includes focal nodular hyperplasia, adenoma, cyst and hemangioma. The prognosis and treatment can be variance for different kinds of liver tumor. Therefore the discrimination between liver tumors is very important. Ultrasonic, computerized axial tomography (CAT) scan, Magnetic Resonance Imaging, and angiography can be used for the liver tumor detection, whereas the pathology is still the most powerful evidence for the liver cancer diagnosis. Grading in the liver cancer is very important for the treatment plan and prognosis. Only liver biopsy image can provide this information. But the criteria for grading are subjective and the effect of the interobserver and intraobserver variability, the results of grading are not always consistent.
In this project, we construct an image database of liver tumor biopsy. Doctors in diagnosis of liver tumor biopsy can query the similar images for the confused case. Doctors may improve the accuracy of the diagnosis by consulting these related anamneses. This system can also be a training platform for the intern.
目錄
中文摘要 I
ABSTRACT II
誌謝 III
表目錄 VI
圖目錄 VII
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 1
1.3 文獻探討 2
1.4 論文架構 3
第二章 理論背景 4
2.1 肝癌病理簡介 4
2.2 肝細胞癌(HEPATOCELLULAR CARCINOMA)玻片影像 5
2.2.1 HCC病理分級法 5
2.2.2 細胞組織染色 7
2.3 細胞核影像分割技術 8
2.3.1 Dual Morphological Reconstruction Method(DMR) 8
2.3.2 分水嶺演算法 9
2.4 細胞核低階特徵擷取 9
第三章 研究方法 14
3.1研究材料 14
3.2系統流程 14
3.2.1 影像分級 16
3.2.2 可靠度分析 17
3.2.3 相似性擷取 19
3.3系統查詢介面 19
第四章 研究結果與討論 21
4.1 SVM的分級結果 21
4.2 研究結果討論 22
4.3查詢案例說明 27
4.3.1 典型影像查詢案例 27
4.3.2 非典型影像查詢案例 28
第五章 結論及未來工作 30
參考文獻 32


表目錄
表1 引發肝癌的危險因素 5
表2 HCC Grading醫學判斷準則 7
表3 細胞核特徵對照表 10
表4 SVM分類參數表 21
表5 系統驗證結果類型歸納表 23
表6 各以單一特徵使用k-NN 分級結果 24
表7 使用CT法計算出的權重參數 25
表8 可靠度分析驗證結果 26
表9 特徵權重檢驗結果 26


圖目錄
圖1 各個Grading的細胞核影像 6
圖2 Grade 1肝癌病理切片 7
圖3 使用DMR進行細胞核輪廓增強的範例 8
圖4 產生細胞核的遮罩的範例以及切割結果 9
圖5 面積不規則性的範例 11
圖6 系統流程圖 15
圖7 相似性擷取查詢畫面 20
圖8 影像相似性擷取查詢結果 20
圖9 影像分級正確率 22
圖10 系統驗證示意圖 22
圖11 使用者上傳影像-典型案例 28
圖12 群聚測試的結果-典型案例 28
圖13 使用者上傳影像-非典型案例 29
圖14 群聚測試的結果-非典型案例 29
圖15 相似性擷取分佈情況-非典型案例 29
[1] Ugochukwu C. Nzeako, Tumor Pathology and Survival in HCC, Cancer, Vol. 74, No. 4, Mar. 1995, pp. 579-588.
[2] Zachary D. Goodman, Kamal G. Ishak, and J. Thomas Stocker, Tumors of the Liver and Intrahepatic Bile Ducts. Washington DC: Armed Forces Institute of Pathology, 2001.
[3] Thomas S. Huang and Yong Rui, “Image retrieval: current techniques, promising directions, and open issues,” Journal of visual Communication and Image Representation 10, pp. 39-62, 1999.
[4] Smeulders, Arnold W.M., “Content-based image retrieval at the end of the early years,” IEEE Trans. Pattern And Machine Intell., 22, pp. 1349-1379, 2000.
[5] Lensink, E., Oon, H., Genitsen, M. and Putten, N., “A Lowcost PACS for Angiography Images,” Computers in Cardiology 1997. (1997) pp 179-182.
[6] Nilsson, A.A., Khanmoradi, H., “A Queuing Model of Picture Archiving and Communication System (PACS) with a Hierarchy of Storage,” Proceedings of Third Annual IEEE. Symposium on Computer-Based Medical Systems. (1990), pp 1-8.
[7] Komatsu K, Tawara K, Nemat M, Osada M and Martinez R. “A High Speed Integrated Computer Network for Picture Archiving and Communication System (PACS),” Proceedings of Third Annual IEEE. Symposium on Computer-Based Medical Systems. 1990, pp 14-23.
[8] Michael Beil, Theano Irinopoulou, Jany Vassy, Gunter Wolf, “A Dual Approach to Structural Texture Analysis in Microscopic Cell Images,” Computer Methods and Programs in Biomedicine, Vol.48, pp. 211-219, 1995.
[9] Benoit Macq, Jean-Philippe Thiran, “Morphological Feature Extraction for the Classification of Digital Images of Cancerous Tissues,” IEEE Transaction on Biomedical Engineering, Vol. 43, NO. 10, pp. 1011-1020, 1996.
[10] Frank, “Computer-Aided Detection of Breast Cancer Nuclei,” IEEE Transaction on Information Technology in Biomedical, Vol. 1, NO. 2, pp. 128-140, 1997.
[11] Abdeltahim Nasser Esgiar, Raouf N. G. Naguib, “Microscopic Image Analysis for Quantitative Measurement and Feature Identification of Normal and Cancerous Colonic Mucosa,” IEEE Transaction on Information Technology in Biomedical, Vol. 2, NO. 3, pp. 197-203, 1998.
[12] Barbara, “Computer-Assisted Differential Diagnosis of Malignant Mesothelioma Based on Syntactic Structure Analysis,” Cytometry, Vol. 35, pp. 23-29, 1999.
[13] Soltanian-Zadeh, H. Jafari-Khouzani, K., “Multiwavelet Grading of Pathological Images of Prostate,” IEEE Transaction on Biomedical Engineering, Vol. 50, NO. 6, pp. 697-704, 2003.
[14] Johannes Mayer, Franz Schweiggert, and Claudia Wittke, “On the Classification of Prostate Carcinoma With Methods from Spatial Statistics,” IEEE Transaction on Biomedical Technology in Biomedicine, Vol. 11, NO. 4, pp. 406-414, 2007.
[15] Scott Doyle, Mark Hwang, Anant Madabhushi, Kinsuk Shah, “Automated Grading of Prostate Cancer using Architectural and Textural Image Features,” IEEE Intl. Symposium on Biomedical Imaging, pp. 1284-1287, 2007.
[16] Ho-Yuen Pang, Ali Tabesh, Mikhail Teverovskiy, “Multifeature Prostate Cancer Diagnosis and Gleason Grading of Histological Images,” IEEE Transaction on Medical Image, pp. 1366-1378, 2007.
[17] S. Fogarasi, A. Kotsianti, V. Kumar, J. Ma, H. Pang, O. Saidi, A. Tabesh, M. Teverovskiy, D. Verbel, and Y. Vengrenyuk, “Improved Prediction of Prostate Cancer Recurrence based on an Automated Tissue Image Analysis System,” IEEE Intl. Symposium on Biomedical Imaging, pp. 257–260,2004.
[18] Shou-Kuo Dai, Yee-Jee Jan, and Yan-Hou Lai, “ Segmentation and Grading of Hepatocellular Carcinoma in Biopsy Images,�耔he 2007 International Conference on Advanced Information Technologies, Apr. 2007.
[19] Bulent Sankur and Mehmet Sezgin, Survey Over Image Thresholding Techniques and Quantitative Performance Evaluation, Journal of Electronic Imaging, Vol. 13, No. 1, Jan. 2004, pp. 146-165.
[20] Arnold Meijster and Jos B.T.M. Roerdink, The Watershed Transform: Definitions, Algorithms and Parallelization Strategies, Fundamenta Informaticae, Vol. 41, No. 1-2, Jan. 2000, pp. 187-228.
[21] Murk J. Bottema, Circularity of Objects in Images, The 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 6, Jun. 2000, pp. 2247-2250.
[22] Hannu Kauppinen, Matti Pietikainen, and Tapio Seppanen, An Experimental Comparison of Autoregressive and Fourier-Based Descriptors in 2D Shape Classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 2, Feb. 1995, pp. 201-207.
[23] M. Garcia-Bonafe, A. Moragas, M. Sans and de Torres, Textural Analysis of Lymphoid cells in serous effusions. A Mathematical Morphologic Approach, Analytical and Quantitative Cytology and Histology, Vol. 15, No. 3, Jun. 1993, pp. 165-170.
[24] Linda G. Shapiro and George C. Stockman, Computer Vision, Prentice Hall, 2001.
[25] Its’hak Dinstein, Robert Haralick, and K. Shanmugam, Texture Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-3, No. 6, Nov. 1973, pp. 610-621.
[26] Robert Haralick, Statistical and Structural Approaches to Texture, Proceedings of the IEEE, Vol. 67, No. 5, May 1979, pp. 786-804.

[27] Chih-Chung Chang and Chih-Jen Lin, LIBSVM: A Library for Support Vector Machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
[28] Menahem Friedman, Abraham Kandel, Introduction to Pattern Recognition, Imperial College Press, 1999, pp. 147-153.
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