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研究生:林昱成
研究生(外文):Yu-Cheng Lin
論文名稱:利用Nakagami分配進行超音波影像參數估計比較-以乳癌影像為例
論文名稱(外文):Using of Nakagami-MRF model for comparing the correlation on parameters for Ultrasound imaging
指導教授:鄭榕鈺林真真林真真引用關係
指導教授(外文):Jung-Yu ChengJen-Jen Lin
學位類別:碩士
校院名稱:銘傳大學
系所名稱:應用統計資訊學系碩士班
學門:數學及統計學門
學類:統計學類
論文出版年:2014
畢業學年度:102
語文別:中文
論文頁數:85
中文關鍵詞:Nakagami-MRF分配相關性最大概似法Nakagami分佈
外文關鍵詞:Nakagami-MRF distributioncorrelationMLENakagami distribution
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在Nakagami 參數影像理論中,係在定義出正方形方框的局部背散射分佈下,利用滑動方框(Sliding Window)的方法來生成 Nakagami 之估計參數圖,其中假設每一塊的正方形方框內資料相互獨立,藉由估計出每一塊方框的 Nakagami-m 參數來生成參數影像,而考慮到在實際情況下,樣本點間應會存在相關性,因此本文中利用Markov Random Field(MRF模式)進行 Nagakami-m 參數估計,此分配的優點主要能考慮到中心資料點與其周圍鄰居點(Neighborhood Point)的交互作用關係,並將與其他參數估計方法進行比較,最後以良惡性腫瘤之乳癌影像資料進行實例分析。
We could try to get more information on the ultrasound imaging by proposed parameter-imaging. Though using the method of sliding window, the parameter-imaging can be obtained by calculating the partial parameters that are the mean of Nakagami distribution. The data in every sliding window was independent, but under the real circumstances, it must create some correlation between observed data. In this study, we considered the correlation between observed data and used Nakagami-MRF model to evaluate the parameter imaging. Then, we compared the parameters-imaging between independent and correlation situation by using Maximum Likelihood Estimate, MLE, Moment Estimate, MME, and Nakgami-MRF Estimate. The data of this study are some real cases of benign and malignant tumors.
誌謝i
中文摘要ii
英文摘要iii
目錄iv
表目錄vi
圖目錄vii
第一章 緒論1
第一節 研究背景與動機1
第二節 研究目的5
第三節 研究步驟5
第四節 研究流程7
第五節 研究範圍與限制8
第二章 文獻探討9
第一節 乳癌超音波與良惡性腫瘤 9
第二節 常用包絡線分佈假設13
第三節 參數估計方法17
第三章 研究方法20
第一節 Nakagami機率密度函數20
第二節 MRF機率密度函數21
第三節 Nakagami-MRF機率密度函數22
第四節 Nakagami-MRF之參數估計23
第五節 無母數估計方法24
第六節 ROC分析26
第四章 結果與分析29
第一節 平均數比較29
第二節 比值比較方法一40
第三節 比值比較方法二50
第五章 結論70
參考文獻72
1.廖尹吟,結合Nakagami參數和輪廓特徵進行乳房超音波的腫瘤分類,清華大學生醫工程與環境科學系醫學物理與工程組碩士論文,2009。
2.余國豪,超音波影像之參數估計_以乳癌為例,銘傳大學應用統計資訊學系研究所碩士論文,2013。
3.Bader, W., Bohmer, S., Leeuwen, P.V., Hackmann, J., Westhof, G., and Hatzmann, W., ” Does texture analysis improve breast ultrasound precision?”, Ultrasound Obstet Gynecol, 15, 2000, p.311–316.
4.Bouhlel, N., Sevestre-Ghalila, S., Rajhi, H., and Hamza, R., “New Markov random field model based on K-distribution for textured ultrasound image”, Medical imaging 2004: ultrasonic imaging and signal processing, in: SPIE International Symposium, 5373, 2004, p.363–372.
5.Bouhlel, N., Sevestre-Ghalila, S., Jaidane, M., and Graffigne, C., “Ultrasound backscatter characterization by using Markov random field model”, in: International Conference on Acoustics, Speech and Signal Processing, ICASSP 2, Toulouse, France, May 14–19, 2006, p.1124–1127.
6.Bouhlel, N., Sevestre-Ghalila, S., ”Nakagami Markov random field as texture model for ultrasound RF envelope image”,Computers in Biology and Medicine,39,2009,p.535-544.
7.Chang, R.F., Wu, W.J., Moon, W.K., and Chen, D.R., “Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors”, Breast Cancer Res Treat, 89, 2005, p.179–185.
8.Chellappa, R., Chatterjee, S., “Classification of textures using Gaussian
Markov random fields”, IEEE Transactionson Acoustics, Speech, and Signal Processing ASSP-33, 4, 1985, p.1–5.
9.Chen, D.R., Chang, R.F., Kuo, W.J., Chen, M.C., and Huang, Y.L., “Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks”, Ultrasound Med Biol, 28, 2002, p.1301–1310.
10.Chen, D.R., Chang, R.F., Chen, C.J., Chen, S.T., Ho, M.F., Hung, S.J., Kuo, S.J., and Moon, W.K., “Classification of breast ultrasound images using fractal feature”, J Clin Imaging 2005a, 29, p.235–245.
11.Chen, D.R., Chen, R.F., Chen, W.M., Chang, C.S., Chen, S.T., Kuo, S.J., and Moon, W.K., “3-D ultrasound texture classification using run difference matrix”, Ultrasound Med Biol 2005b, 31, p.763–770.
12.Chou, Y.H., Tiu, C.M., Hung, G.S., Wu, S.C., Chang, T.Y., and Chiang, H.K., “Stepwise logistic regression analysis of tumor contour features for breast ultrasound diagnosis”, Ultrasound Med Biol, 27, 2001, p.1493–1498.
13.Cramblitt, R.M., Parker, K.J., “Generation of non-Rayleigh speckle distribution using marked regularity models”, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 46, 4, 1999, p.867–873.
14.David, W.H., Stanley, L., “Applied Logisitc Regression ( ed.)”, Wiley-Interscience Publication, 6, 2000.
15.Dutt, V., Greenleaf, J.F., “Ultrasound echo envelope analysis using a homodyned K-distribution signal model”, Ultrasonic Imaging, 6, 1994, p.265–287.
16.Eltoft, T., “The Rician inverse Gaussian distribution: a new model for non-Rayleigh signal amplitude statistics”, IEEE Transactions on Image Processing, 14, 11, 2005.
17.Goodman, J.W., “Statistical properties of laser speckle patterns”, in: J.C. Dainty (Ed.), Laser Speckle and Related Phenomena ( ed.), Springer, Berlin, 9, Topics in Applied Physics, 1984, p.976.
18.Harper, P.A., Kelly-Fry, E., Noe, J.S., Bies, J.R., and Jackson, V.P., “Ultrasound evaluation of solid breast masses”, Radiology, 146, 1983, p.731–736.
19.Huang, Y.L., Wang, K.L., and Chen, D.R., “Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines”, Neural Comput Appl, 15, 2006, p.164–169.
20.Huang, Y.L., “Computer-aided diagnosis using neural networks and support
vector machines for breast ultrasonography”, J Med Ultrasound, 17, 2009,
p.17–24.
21.Jackson, V.P., “The role of US in breast imaging”, Radiology, 177, 1990,
p.305–311.
22.Jackson, V.P., “Management of solid breast nodules: What is the role of
sonography?” , Radiology, 196, 1995, p.14–15.
23.Jackson, V.P., Reynolds, H.E., and Hawes, D.R., “Sonography of the breast”, Semin Ultrasound CT MR, 17, 1996, p.460–475.
24.Kashiwase, Y., Morioka, J., Inamura, H., Yoshizawa, Y., Usui, R., and Kurosawa, M., “Quantitative Analysis of mast cells in benign and malignant breast lesions”, Int Arch Allergy Immunol, 134, 2004, p.199–205.
25.Karmeshua, R.A., “Ultrasonic backscattering in tissue: characterization through Nakagami-generalized inverse Gaussian distribution”, Computers in Biology and Medicine, 37, 2, 2007, p.166–172.
26.Kolb, T.M., Lichy, J., and Newhouse, J.H., “Occult cancer in women with dense breasts: Detection with screening US-diagnostic yield and tumor characteristics”,Radiology, 207, 1998, p.191–199.
27.Kuo, W.J., Chang, R.F., Moon, W.K., Lee, C.C., and Chen, D.R., “Computer-aided diagnosis of breast tumors with different US systems”, Acad Radiol, 9, 2002, p.793–799.
28.Li, S.Z., “Markov Random Field Modeling in Computer Vision”, Springer, New York, 2001.
29.Ma, L., Fishell, E., Wright, B., Hanna, W., Allan, S., and Boyd, N.F., “Case-control study of factors associated with failure to detect breast cancer by mammography”, J Natl Cancer Inst, 84, 1992, p.781–785.
30.Moore, S.K., “Better breast cancer detection”, IEEE Spectrum, 38, 2001,
p.50–54.
31.Paulinelli, R.R., Freitas-Junior, R., Moreira, M.A., Moraes, V.A., Bernardes-Junior, J.R., Vidal, C.S., Ruiz, A.N., and Lucato, M.T., “Risk of malignancy in solid breast nodules according to their sonographic features”, J Ultrasound Med, 24, 2005, p.635–641.
32.Raju, B.I., Srinivasan, M.A., “Statistics of envelope of high-frequency ultrasonic backscatter from human skin in vivo”, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Contro, 49, 7, 2002, p.1–5.
33.Rudland, P.S., Leinster, S.J., Winstanley, J., Green, B., Atkinson, M., and Zakhour, H.D., “Immunocytochemical identification of cell types in benign and malignant breast diseases: Variations in cell markers accompany the malignant state”, J Histochem Cytochem, 41, 1993, p.543–553.
34.Sehgal, C.M., Weinstein, S.P., Arger, P.H., and Conant, E.F., “A review of breast ultrasound”, J Mammary Gland Biol Neoplasia, 11, 2006, p.113–123.
35.Shankar, P. M., “A general statistical model for ultrasonic backscattering from tissues”, IEEE Trans. Ultrason. Ferroelech. Freq Control, 47, 2000, p.727-736.
36.Shankar, P.M., Dumane, V.A., Reid, J.M., Genis, V., Forsberg, F., Piccoli, C.W., and Goldberg, B.B., “Classification of ultrasonic B-mode images of breast masses using Nakagami distribution”, IEEE Trans Ultrason Ferroelectr Freq Control, 48, 2001, p.569–580.
37.Shankar, P.M., “A model for ultrasonic scattering from tissues based on the K-distribution”, Physics in Medicine and Biology, 40, 1995, p.1633–1649.
38.Shankar, P.M., “Ultrasonic tissue characterization using a generalized Nakagami model”, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 48, 2001, p.1716–1720.
39.Sickles, E.A., Filly, R.A., and Callen, P.W., “Benign breast lesions: Ultrasound detection and diagnosis”, Radiology, 151, 1984, p.467–470.
40.Skaane, P., Engedal, K., “Analysis of sonographic features in the differentiation of fibroadenoma and invasive ductal carcinoma”, AJR Am J Roentgenol, 170, 1998, p.109–114.
41.Smolikova, R., Wachowiaka, M.P., and Zurada, J.M., “An information-theoretic approach to estimating ultrasound backscatter characteristics”, Computers in Biology and Medicine, 34, 2004, p.355–370.
42.Stavros, A.T., Thickman, D., Rapp, C.L., Dennis, M.A., Parker, S.H., and Sisney, G.A.. “Solid breast nodules: Use of sonography to distinguish between benign and malignant lesions”, Radiology, 196, 1995, p.123–134.
43.Tsui, P.H., Wang, S.H., Huang, C.C., and Chiu, C.Y., “Quantitative analysis of noise influence on the detection of scatterer concentration by Nakagami parameter”, J Med Biol Eng, 25, 2005, p.45–51.
44.Tsui, P.H., Chang, C.C., “Imaging local scatterer concentrations by the Nakagami statistical model”, Ultrasound Med Biol, 33, 2007, p.608–619.
45.Tsui, P.H., Huang, C.C., Chang, C.C., Wang, S.H., and Shung, K.K., “Feasibility study of using high-frequency ultrasonic Nakagami imaging for characterizing the cataract lens in vitro”, Phys Med Biol, 52, 2007, p.6413–6425.
46.Tsui, P.H., Yeh, C.K., Chang, C.C., and Liao, Y.Y., “Classification of breast masses by ultrasonic Nakagami imaging”, Phys Med Biol 2008a, 53,
p.6027–6044.
47.Tsui, P.H., Yeh, C.K., Chang, C.C., Chen, W.S., “Performance evaluation of ultrasonic Nakagami image in tissue characterization”, Ultrason Imaging 2008b, 30, p.78–94.
48.Tsui, P.H., Yeh, C.K., Liao, Y.Y., Chang, C.C., Kuo, W.H., Chang, K.J., and Chen, C.N., “Ultrasonic Nakagami imaging: A strategy to visualize the scatterer properties of benign and malignant breast tumors”, Ultrasound in Med. & Biol., 36, 2, 2010, p.209-217.
49.Wagner, R.F., Smith, S.W., Sandrik, J.M., and Lopez, H., “Statistics of speckle in ultrasound B-scans”, IEEE Transactions on Sonics and Ultrasonics US-30, 1986, p.156–163.
50.Wagner, R.F., Insana, M.F., Gara, B.S., Brown, D.G., and Shawker, T.H.,
“Analysis of ultrasound image texture via generalized Rician statistics”, Optical Engineering, 25, 1986, p.743–748.
51.Zonderland, H.M., “The role of ultrasound in the diagnosis of breast cancer”, Semin Ultrasound CT MR, 21, 2000, p.317–324.
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