(3.237.97.64) 您好!臺灣時間:2021/03/04 14:05
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:陳亮勳
研究生(外文):Chen, Liang-Xun
論文名稱:使用空間相關性的高斯混合模型對PET/CT影像作分割
論文名稱(外文):Segmentation of PET/CT Images Using Spatial Dependence in Gaussian Mixture Model
指導教授:盧鴻興盧鴻興引用關係
指導教授(外文):Lu, Herry Horng-Shing
學位類別:碩士
校院名稱:國立交通大學
系所名稱:統計學研究所
學門:數學及統計學門
學類:統計學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:英文
論文頁數:25
中文關鍵詞:高斯混合模型空間相關性PET/CT影像分割
外文關鍵詞:Gaussian mixture modelSpatial dependencePET/CTImage segmentation
相關次數:
  • 被引用被引用:0
  • 點閱點閱:139
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
PET影像中,高亮度的區塊常被視為疑似腫瘤產生的地方。若能將PET影像中的腫瘤部位精確的分割出來,將會對醫生的診療有很大的幫助。近年來,由於PET/CT的發明,它結合了PET 和CT的優點,能將腫瘤細胞的活動狀況及位置融合在同一張影像中,使醫生對腫瘤診斷有更進一步的發展。而本研究在分割PET影像的同時也加進了CT影像的資訊,目的是希望能將腫瘤細胞更精確的分割出來。
我們使用Gaussian mixture model (GMM) 對PET和CT的融合影像作分割。此外,我們考慮了PET和CT的相關性,使用一個二維的GMM對PET/CT影像作分割。我們還在GMM中加入空間相關性,將影像的中每一個像素都考慮它們的鄰近點,然後使用一個多維的GMM模型去配適。這些方法的結果均較單對PET影像作分割的結果為佳。

The specific brightened regions of PET images are the suspected regions of tumor. Segmenting the region of tumor on PET images will be very helpful to doctors. In recent years, the invention of Positron emission tomography/computed tomography (PET/CT) has allowed combination of the advantages of PET and CT: namely, that the activities and location of tumor cells can be merged in one image. This merged images provides significant progress for doctors diagnosing tumors. In this study, the information of CT is used when segmenting the PET images. The aim is to enhance accuracy of segmenting the regions of tumor.
The fusion images of PET and CT are segmented by Gaussian mixture model (GMM). In addition, a two-dimensional GMM is used to fit the PET and CT image data by considering the correlation of PET and CT. The spatial dependence is also considered in a GMM. Our approach is to consider points surrounding each pixel to fit a multi-dimensional GMM to the data. These methods all have better performance than the result of only implementing segmenting PET images in this study.

Chapter 1 Introduction 1
Chapter 2 Methodologies 1
2.1 Gaussian Mixture Model (GMM) 3
2.2 Using Spatial Dependence in GMM 6
2.3 Determine the Initial Parameters of GMM 8
2.4 Bayesian Information Criterion for Model Selection 10
2.5 The Method of Fused PET and CT Images 10
2.6 F-measure 11
2.7 Procedure 12
Chapter 3 Phantom Study 14
Chapter 4 Conclusions 23
Reference 24


[1] Akaike, H. (1974). A new look at the statistical model identification. IEEE Trans. Automat. Ontr., 19, 716-723.
[2] Dempster, A.P., Laird, N.M., Rubin, D.B. (1977). Maximum likelihood from incomplete datavia the EM algorithm (with discussion). Journal of the Royal Statistical Society, B, 39, 1-38.
[3] Engel, J., Herrmann, E., Gasser, T. (1994). An iterative bandwidth selector for kernel estimation of densities and their derivatives. Journal of Nonparametric Statistics, 4, 21–34.
[4] Hsiao, I.T., Rangarajan, A., Gindi, G. (1998). Joint-Map Reconstruction/Segmentation for transmission Tomography Using Mixture-Model as Priors. Proc. IEEE Nuclear Science Symposium and Medical Image Conference, 3, 1689-1693.
[5] Chiang H.Y. (2004). Segmentation of Dynamic MicroPET Images by K-mean and Mixture Methods. Master’s thesis, Institute of Statistics National Chiao Tung University.
[6] McLachlan, G.J., Basford, K.E. (1988). Mixture Model: Inference and Applications to Clustering. Marcel Dekker, New York.
[7] Ye M.C. (2009). Segmentation of PET/CT images by Flexible Mixture Models and Comparison with K-means and Gaussian Mixture Models. Master’s thesis, Institute of Statistics National Chiao Tung University.
[8] Olinger, J.M., Fessler, J.A. (1997). Positron emission tomography. IEEE Signal Processing Magazine, 14, 43-55.
[9] Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics. 6, 461–464.
[10] Sheather, S.J., Jones, M.C. (1991). A reliable data-based bandwidth selection method for kernel density estimation. Journal of Royal Statistics Society. B, 53, 683-690.
[11] Silverman, B. W. (1986) Density Estimation. Chapman and Hall, London.
[12] Chen T.B. (2007). Statistical Applications of Maximized Likelihood Estimates with the Expectation-Maximization Algorithm for Reconstruction and Segmentation of MicroPET and Spotted Microarray Images. Ph.D. Dissertation, Institute of Statistics National Chiao Tung University.
[13] Townsend, D. W., Carney, J.P., Yap, J.T, Hall, N. C. (2004). PET/CT Today and Tomorrow. The Journal of Nuclear Medicine, 45, 4S-14S.
[14] Van Rijsbergen, C. J. (1979). Information Retrieval. London. 2nd Edition. Butterworth. London, England.
[15] Vardi, Y., Shepp, La., Kaufman, L. (1985). A statistical model for positron emission tomography. Journal of the American Statistics Association, 80, 8-20.
[16] Wong, K.P., Feng D., Meikle, S.R., Fulham, M.J. (2002). Segmentation of Dynamic PET Images Using Cluster Analysis. IEEE Transactions on Nuclear Science, 49, 200-207.

連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔