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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳中明 | |
dc.contributor.author | I-Lin Tsai | en |
dc.contributor.author | 蔡漪琳 | zh_TW |
dc.date.accessioned | 2021-06-13T06:39:09Z | - |
dc.date.available | 2005-08-09 | |
dc.date.copyright | 2005-08-09 | |
dc.date.issued | 2005 | |
dc.date.submitted | 2005-08-07 | |
dc.identifier.citation | http://www.doh.gov.tw/statistic/index.htm
2. Yang PM, Huang GT, Lin JT, et al. Ultrasonography in the diagnosis of benign diffuse parenchymal liver diseases: A prospective study. J Formosan Med Assoc 1988; 87: 966-977. 3. Goyal AK, Pokharna DS, Sharma SK. Ultrasonic diagnosis of cirrhosis reference to quantitative measurements of hepatic dimensions. Gastrointest Radiol 1990; 15: 32-34. 4. Vilgrain V, Lebrec D, Menu Y, Scherrer A, Nahum H. Comparison between ultrasonographic signs and the degree of portal hypertension in patients with cirrhosis. Gastrointest Radiol 1990; 15: 218-222. 5. Joseph AEA, Saverymuttu SH, Al-Sam S, Cook MG, Maxwell JD. Comparison of liver histology with ultrasonography in assessing diffuse parenchymal liver disease. Clinical Radiology 1991; 43: 26-31. 6. Ferral H, Male R, Cardiel M, Munoz L, Ferrari FQ. Cirrhosis: Diagnosis by liver surface analysis with high-frequency ultrasound. Gastrointest Radiol 1992; 17: 74-78. 7. Gaiani S, Gramantieri L, Venturoli N, et al. What is the criterion for differentiating chronic hepatitis from compensated cirrhosis? A prospective study comparing ultrasonography and percutaneous liver biopsy. Journal of Hepatology 1997; 27: 979-985. 8. Aubé C, Oberti F, Korali N, et al. Ultrasonographic diagnosis of hepatic fibrosis or cirrhosis. Journal of Hepatology 1999; 30: 472-478. 9. Xu Y, Wang B, Cao H. An ultrasound scoring system for the diagnosis of liver fibrosis and cirrhosis. Chinese Medical Journal 1999; 112: 1125-1128. 10. Lang M, Ermert H, Heuser L. In-vivo study of on-line liver tissue classification based on envelope power spectrum analysis. Ultrasonic Symposium Proceedings 1990; 3: 1345-1348. 11. Akiyama I, Saito T, Nakamura M. Taniguchi N, Itoh K. Tissue characterization by using fractal dimension of B-scan image. Ultrasonic Symposium Proceedings 1990; 3: 1353-1355. 12. Wu CM, Chen YC, Hsieh KS. Texture features for classification of ultrasonic liver images. IEEE Trans. Med. Imaging 1992; 11: 141-152. 13. Botros NM. A PC-Based tissue classification system using artificial neural networks. IEEE Trans. Instrumentation and Measurement 1992; 41: 633-638. 14. Cohen FS, Georgiou G. Detecting and estimating structure regularity of soft tissue organs from ultrasound images. Proceedings of IEEE International Conference on Image Processing 1995; 2: 488-491. 15. Pavlopoulos S, Konnis G, Kyriacou E, Koutsouris D, Zoumpoulis P, Theotokas I. Evaluation of texture analysis techniques for quantitative characterization of ultrasonic liver images. Proceedings of Annual International Conference of the IEEE EMBS 1996; 3: 1151-1152. 16. Bleck JS, Ranft U, Gebel M, et al. Random field models in the textural analysis of ultrasonic images of the liver,” IEEE Trans. Med. Imaging 1996; 15: 796-801. 17. Kadah YM, Farag AA, Zurada JM, Badawi AM, Youssef AM. Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images. IEEE Trans. Med. Imaging 1996; 15: 466-478. 18. Sun YN, Horng MH, Lin XZ, Wang JY. Ultrasonic image analysis for liver diagnosis: A noninvasive alternative to determine liver disease. IEEE Engineering in Medicine and Biology 1996; 15: 93-101. 19. Kyriacou E, Pavlopoulos S, Konnis G, Koutsouris D, Zoumpoulis P, Theotokas I. Computer assisted characterization of diffused liver disease using image texture analysis techniques on B-Scan. Conference Record of IEEE Nuclear Science Symposium & Medical Imaging Conferenc 1997; 2: 1479-1483. 20. Fukushima M, Ogawa K, Kubota T, Hisa N. Quantitative tissue characterization of diffuse liver diseases from ultrasound images by neural network. Conference Record of IEEE Nuclear Science Symposium & Medical Imaging Conference 1998; 2: 1233-1236. 21. Mojsilovic A, Popovic M, Markovic S, Krstic M. Characterization of visually similar diffuse diseases from B-scan liver images using nonseparable wavelet transform. IEEE Trans. Med. Imaging 1998; 17: 541-549. 22. Ogawa K, Fukushima M, Kubota K, Hisa N. Computer-aided diagnostic system for diffuse liver diseases with ultrasonography by neural networks. IEEE Trans. Nuclear Science 1998; 45: 3069-3074 23. Hong MH, Sun YN, Lin XZ. Texture feature coding method for classification of liver sonography. Computer Med. Imaging and Graphics 2002; 26: 33-42. 24. Wen-Chun Yeh, Sheng-Wen Huang and Pai-chi Li. Liver fibrosis grade classification with B-mode ultrasound. Ultrasound in Med. & Biol. 2003; 29: 1229-1235. 25. Gauch JM. Image segmentation and analysis via multiscale gradient watershed hierarchies. IEEE Trans. Image Processing 1999; 8: 69-79. 26. Vincent L, Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulation. IEEE Trans. PAMI 1991; 13: 583-597. 27. Karaman K, Kutay M, Bozdagi G. An adaptive speckle suppression filter for medical ultrasonic imaging. IEEE Trans. Med. Imaging 1995; 14: 283-292. 28. Keller JM, Chen S, Grownover RM. Texture description and segmentation through fractal geometry. Comput Vis Graph Image Process 1989; 45: 150-166. 29. Landini G, Rippin JW. How important is tumor shape? Quantification of the epithelial-connective tissue interface in oral lesions using local connected fractal dimension analysis. J Pathol 1996; 179: 210-217. 30. Daugman JG. Two-dimensional spectral analysis of cortical receptive field profiles. Vision Research 1980; 20:847-856. 31. Cherkassky V, Mulier F. Learning from data: concepts, theory, and methods. New York: John Wiley & Sons, 1998. 32. Cooley WW, Lohnes PR. Multivariate data analysis. New York : Wiley, 1971 33. Li KC. Sliced inverse regression for dimension reduction,(with discussions). J Amer Statist Assoc 1991; 86: 316-342. 34. Dillon WR, Goldstein M. Multivariate analysis: method and applications. New York: John Wiley & Sons, 1984. 35. Cristianini N, Shawe-Taylor J. An introduction to support vector machine. Cambridge: Cambridge University Press, 2000. 36. Zurada JM. Introduction to artificial neural systems. Boston: PWS Publishing Company, 1992. 37. Huang HC, Chen CM, Wang SD. Adaptive ultrasonic speckle reduction based on the slope facet model. Ultrasound in Medicine and Biology, revised, 2003. 38. Chen CM, Lu HHS, Hsiao AT. A dual snake model of high penetrability for ultrasound image boundary extraction. Ultrasound in Medicine and Biology 2001; 27: 1651-1665. 39. Chen CM, Chou YH, Han KC, Hung GS. Computer-aided diagnosis of breast lesions on sonogram using nearly setting-independent features and artificial neural networks. Radiology, to appear, Feb. 2003. 40. 國立台灣大學醫學工程研究所 許淑雅碩士論文 : 肝硬化超音波影像之電腦輔助診斷 : 幾近不受參數設定影響之影像特徵擷取 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/35028 | - |
dc.description.abstract | 本論文是不受機器參數影響的超音波肝臟影像分類之研究,為了描述肝臟影像的紋理特徵,我們透過watershed transform等影像處理步驟,去找出如cell number、與cell size等影像的cell-based特徵,另外還有region size、以及常見的grey-level co-occurrence matrix特徵值。在這些特徵值的設計上,我們都會以脾臟的特徵作為肝臟特徵的正規化參考,以消除不同人之間的潛在差別,並使不同機器參數設定下的影像之間得以正規化。而最後所找到的所有特徵值,我們會繼續透過forward selection的處理去找到一個能夠表現最佳分類效能的特徵值組合作為最後分類的數據。而我們是利用這些特徵值來將影像分為兩類-肝硬化影像、與正常肝臟影像。
本研究的分類方法上,是採用leave-one-out 的logistic regression function來產生分類器。我們所使用的資料一共有273筆,皆是由台大醫院所提供。其中有134筆是肝硬化影像、另外的139筆是正常肝臟影像。最後得到的分類結果有93%的正確率;且在本研究中,我們另外提出兩種方法(Ogawa的方法、與nonseperable wavelet decompositions)來進行結果的比較,而我們所提出的方法顯現了最好的分類正確率。為了驗證採用脾臟作為正規化參考的功效,在本研究中去計算了沒有脾臟特徵來作為參考時的特徵組合的分類結果,其正確率為91%。因此得出沒有脾臟作為正規化參考的分類結果較有脾臟作為正規化參考的結果來得不好的結論。也可用來說明本研究所提出的特徵值是需要以脾臟作為正規化參考的。 | zh_TW |
dc.description.abstract | The classification of ultrasonic liver images is studied, making use of the features of cell-based algorithms, region size and grey-level co-occurrence matrix features to describe the texture characterization problem. We use the ultrasonic texture of the same patient’s spleen image as the reference in reading that of the liver image for the purpose of normalization the texture variation caused from different system settings. The resulting features are further processed with forward selection to obtain a combination of features which represents the most discriminating pattern space for classification. The extracted features are used to classify two sets of ultrasonic liver images-normal liver, and cirrhotic liver.
The leave-one-out logistic regression function classifier is employed to evaluate the performance of these features. The proposed method was evaluated by using 273 clinical ultrasound images collected at National Taiwan University Hospital, including 139 normal livers and 134 cirrhotic livers. The classification accuracy was 93%. The proposed method was compared to other texture description methods using only images from livers (Ogawa’s method and nonseperable wavelet decompositions). The proposed method gave the highest classification rate, showing its applicability for the approach based analysis of the large class of natural textures. We also modified the features to be without spleen reference to verify the effect of normalization by reference to spleen images. And the classification accuracy was 91%. Obviously the proposed method has better classification performance when it referenced to the spleen images. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T06:39:09Z (GMT). No. of bitstreams: 1 ntu-94-R92548055-1.pdf: 1314787 bytes, checksum: 6122352999c82505a034ccdbf7e146cc (MD5) Previous issue date: 2005 | en |
dc.description.tableofcontents | 第一章 研究背景與文獻探討 1
1.1 臨床影像特徵的發掘與組合 2 1.2 電腦輔助診斷與數學影像特徵 5 第二章 特徵擷取 8 2.1 資料準備 10 2.2 選取ROI影像 10 2.3 Normalization 11 2.4 Co-occurrence Matrix 12 2.5 Cell-based 數學影像特徵 14 2.5.1 Multi-Scale Gaussian Filter 14 2.5.2 Laplacian Filter 16 2.5.3 Gray Scale Dilation 17 2.5.4 Watershed Transform 18 2.5.5 Contrast 特徵 22 2.5.6 Cell Erosion 23 2.5.6.1 Merge Cells 23 2.5.6.2 Erosion 25 2.6 Region Size 數學影像特徵 30 2.7 Fractal Dimension 數學影像特徵 33 2.8 多重解析度之數學影像特徵 34 第三章 方法的比較 37 3.1 Ogawa的方法 37 3.2 Yeh et. al的方法 40 第四章 特徵值選取與分類 45 4.1 特徵值選取 45 4.2 分類方法 47 4.2.1 Logistic Regression Function 47 4.2.2 Cross-validation 52 第五章 實驗結果與討論 54 5.1 資料來源 54 5.2 特徵值 55 5.3 結果 56 5.4 結果比較 64 5.4.1 Ogawa的結果 64 5.4.2 Yeh et. al的結果 65 第六章 結論與展望 66 參考文獻……………………………………………………………………………..67 | |
dc.language.iso | zh-TW | |
dc.title | 以肝臟超音波影像紋理特徵區別肝硬化之研究 | zh_TW |
dc.type | Thesis | |
dc.date.schoolyear | 93-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 楊培銘 | |
dc.contributor.oralexamcommittee | 孫永年 | |
dc.subject.keyword | 肝硬化,超音波,分類,迴歸分析函數,特徵值, | zh_TW |
dc.subject.keyword | Liver Cirrhosis,Ultrasound,Classification,Logistic Regression Function,Texture Features, | en |
dc.relation.page | 71 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2005-08-08 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
顯示於系所單位: | 醫學工程學研究所 |
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