請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/64386
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張恆華(Herng-Hua Chang) | |
dc.contributor.author | Hung-Ting Liu | en |
dc.contributor.author | 劉泓廷 | zh_TW |
dc.date.accessioned | 2021-06-16T17:44:14Z | - |
dc.date.available | 2017-08-17 | |
dc.date.copyright | 2012-08-17 | |
dc.date.issued | 2012 | |
dc.date.submitted | 2012-08-14 | |
dc.identifier.citation | [1] Jasjit S. Suri, Aly A. Farag, Evangelia Micheli-Tzanakou, Bipul Das, and Swapna Banerjee. Parametric contour model in medical image segmentation. In Deformable Models, Topics in Biomedical Engineering. International Book Series, pages 31-74. Springer New York, 2007.
[2] L. J. Erasmus, D. Hurter, M. Naude, H. G. Kritzinger, and S. Acho. A short overview of mri artefacts. South African Journal of Radiology, 8(2):13-17, 2004. [3] F. Segonne, A.M. Dale, E. Busa, M. Glessner, D. Salat, H.K. Hahn, and B. Fischl. A hybrid approach to the skull stripping problem in mri. NeuroImage, 22(3):1060-1075, 2004. [4] Sandip Basu, Habib Zaidi, Mohamed Houseni, Gonca Bural, Jay Udupa, Paul Ac-ton, Drew A. Torigian, and Abass Alavi. Novel quantitative techniques for assess-ing regional and global function and structure based on modern imaging modalities: Implications for normal variation, aging and diseased states. Seminars in Nuclear Medicine, 37(3):223-239, 2007. [5] Seattle cancer care alliance. http://www.seattlecca.org/. [6] Yue Hua Wang and Yi Li Fu. Research on segmentation methods of brain using mri images. Energy Procedia, 11(0):2382 -- 2388, 2011. 2011 International Conference on Energy and Environmental Science - ICEES 2011. [7] Rik Stokking, Koen L. Vincken, and Max A. Viergever. Automatic morphology-based brain segmentation (mbrase) from mri-t1 data. NeuroImage, 12(6):726-738, 2000. [8] John Chiverton, Kevin Wells, Emma Lewis, Chao Chen, Barbara Podda, and De-clan Johnson. Statistical morphological skull stripping of adult and infant mri data. Computers in Biology and Medicine, 37(3):342-357, 2007. [9] David W. Shattuck, Stephanie R. Sandor-Leahy, Kirt A. Schaper, David A. Rotten-berg, and Richard M. Leahy. Magnetic resonance image tissue classification using a partial volume model. NeuroImage, 13(5):856-876, 2001. [10] Betsabeh Tanoori, Zohreh Azimifar, Alireza Shakibafar, and Sarajodin Katebi. Brain volumetry: An active contour model-based segmentation followed by svm-based classification. Computers in Biology and Medicine, 41(8):619-632, 2011. [11] James C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell, MA, USA, 1981. [12] R. Krishnapuram and J.M. Keller. A possibilistic approach to clustering. Fuzzy Systems, IEEE Transactions on, 1(2):98-110, 1993. [13] N.R. Pal, K. Pal, and J.C. Bezdek. A mixed c-means clustering model. In Fuzzy Systems, 1997., Proceedings of the Sixth IEEE International Conference on, volume 1, pages 11-21, 1997. [14] N.R. Pal, K. Pal, J.M. Keller, and J.C. Bezdek. A possibilistic fuzzy c-means clustering algorithm. Fuzzy Systems, IEEE Transactions on, 13(4):517-530, 2005. [15] Michael Kass, Andrew Witkin, and Demetri Terzopoulos. Snakes: Active contour models. International Journal of Computer Vision, 1(4):321-331, 1988. [16] Laurent D. Cohen. On active contour models and balloons. CVGIP: Image Underst., 53:211-218, 1991. [17] L.D. Cohen and I. Cohen. Finite-element methods for active contour models and balloons for 2-d and 3-d images. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 15(11):1131-1147, 1993. [18] S. Phumeechanya, C. Pluempitiwiriyawej, and S. Thongvigitmanee. Edge type-selectable active contour using local regional information on extendable search lines. In Image Processing (ICIP), 2010 17th IEEE International Conference on, pages 653-656, 2010. [19] Zhi Gang Zheng, Liang Wu, and Yun'an Hu. An improved balloon snake method for road contour extraction. In Computer Application and System Modeling (ICCASM), 2010 International Conference on, volume 3, pages 227-231, 2010. [20] Chen Yang Xu and J.L. Prince. Gradient vector flow: a new external force for snakes. In Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on, pages 66-71, 1997. [21] Yuan Quan Wang, Li Xiong Liu, Hua Zhang, Zuo Liang Cao, and Shao Pei Lu. Image segmentation using active contours with normally biased gvf external force. Signal Processing Letters, IEEE, 17(10):875-878, 2010. [22] A.K. Jumaat, W.E.Z.W.A. Rahman, A. Ibrahim, and R. Mahmud. Comparison of balloon snake and gvf snake in segmenting masses from breast ultrasound images. In Computer Research and Development, 2010 Second International Conference on, pages 505-509, 2010. [23] Chong Sze Tong, Pong C Yuen, and Y. Y. Wong. Dividing snake algorithm for multiple object segmentation. Optical Engineering, 41:3177-3182, 2002. [24] Chun Ming Li, Jun Dong Liu, and M.D. Fox. Segmentation of edge preserving gradient vector flow: an approach toward automatically initializing and splitting of snakes. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Com-puter Society Conference on, volume 1, pages 162-167, 2005. [25] M. Charfi. Using the ggvf for automatic initialization and splitting snake model. In I/V Communications and Mobile Network (ISVC), 2010 5th International Symposium on, pages 1-4, 2010. [26] Stephen M. Smith. Fast robust automated brain extraction. Human Brain Mapping, 17(3):143-155, 2002. [27] Audrey H. Zhuang, Daniel J. Valentino, and Arthur W. Toga. Skull-stripping magnetic resonance brain images using a model-based level set. NeuroImage, 32(1):79-92, 2006. [28] Stanley Osher and James A Sethian. Fronts propagating with curvature-dependent speed: Algorithms based on hamilton-jacobi formulations. Journal of Computational Physics, 79(1):12-49, 1988. [29] Li Zheng and Tao Li. Semi-supervised hierarchical clustering. In Data Mining (ICDM), 2011 IEEE 11th International Conference on, pages 982-991, 2011. [30] Shan Guo Peng, Xi Wu Wang, and Qi Gen Zhong. The study of em algorithm based on forward sampling. In Electronics, Communications and Control (ICECC), 2011 International Conference on, pages 4597-4600, 2011. [31] Juan Ying Xie and Shuai Jiang. A simple and fast algorithm for global k-means clustering. In Education Technology and Computer Science (ETCS), 2010 Second International Workshop on, volume 2, pages 36-40, 2010. [32] M. Barni, V. Cappellini, and A. Mecocci. Comments on 'a possibilistic approach to clustering'. Fuzzy Systems, IEEE Transactions on, 4(3):393-396, 1996. [33] C. Tomasi and R. Manduchi. Bilateral filtering for gray and color images. In Com-puter Vision, 1998. Sixth International Conference on, pages 839-846, 1998. [34] Xiao Dong Tao and Ming Ching Chang. A skull stripping method using deformable surface and tissue classification. In Proc. SPIE, volume 7623, 2010. [35] Craig A. Ellis and Simon A. Parbery. Is smarter better? a comparison of adaptive, and simple moving average trading strategies. Research in International Business and Finance, 19(3):399-411, 2005. [36] Yu Dong Wang, Chong Feng Wu, and Zhi Yuan Pan. Multifractal detrending moving average analysis on the us dollar exchange rates. Physica A: Statistical Mechanics and its Applications, 390(20):3512-3523, 2011. [37] Yashil. http://yashil.20m.com/. [38] rkkumar. http://www.seas.harvard.edu/~rkkumar. [39] Internet brain segmentation repository. http://www.cma.mgh.harvard.edu/ibsr/. [40] Herng Hua Chang, Audrey H. Zhuang, Daniel J. Valentino, and Woei Chyn Chu. Performance measure characterization for evaluating neuroimage segmentation al-gorithms. NeuroImage, 47(1):122-135, 2009. [41] A. Fenster and B. Chiu. Evaluation of segmentation algorithms for medical imag-ing. In Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the, pages 7186-7189, 2005. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/64386 | - |
dc.description.abstract | 醫學影像分割是個一直以來被廣泛研究的領域。在這篇論文中,我們提出一個新的全自動參數化主動輪廓模型 (Parametric Active Contour) 彈性蛇變曲線來進行腦部區域的擷取。我們所提出的方法其架構主要分成兩個部分: 影像前處理和影像分割。首先,利用模糊可能性分群模型 (FPCM) 對腦部影像進行分類,其分類後的標記影像為後續輪廓初始化步驟的主要參考依據。在第二個部分裡,藉由參考前一階段所計算出來的標記影像,將輪廓初始化在腦部區域的外圍,並在彈性力的驅使下逐步變形。彈性蛇變曲線模型利用可適性法向力使得輪廓向內收斂以擷取腦部區域的邊界。我們將此方法應用在 T1-權重的核磁共振影像上的腦部分割,並在和其他著名的腦部區域分割方法比較之下,顯示我們所提出的方法具有良好的分割結果且具有廣泛的醫學影像分割應用潛能。 | zh_TW |
dc.description.abstract | Brain image segmentation, also known as skull-stripping, has been the focus of a wide variety of research in recent years. This paper proposes a new automatic parametric active contour model (snake) for brain image extraction. The proposed framework consists of two stages: image preprocessing and image segmentation. First, the fuzzy possibilistic c-means (FPCM) is used for voxel clustering, which provides a labeled image for the following contour initialization. At the second stage, the contour is initialized outside the brain surface based on the result of the FPCM and evolves under the guidance of the balloon force. The balloon snake model drives the contour with an adaptive inward normal force to capture the boundary of the brain. The proposed algorithm is evaluated by segmenting a number of T1-weighted magnetic resonance images. Experimental results and comparisons with other existing approaches show the effectiveness of this new scheme and potential applications in a wide variety of brain image segmentation. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T17:44:14Z (GMT). No. of bitstreams: 1 ntu-101-R99525086-1.pdf: 2396553 bytes, checksum: 7a644ddc4b24b5e6e653f6ad0b61bdda (MD5) Previous issue date: 2012 | en |
dc.description.tableofcontents | 1 INTRODUCTION 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 RELATED STUDIES 6 2.1 Brain Surface Extractor . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Brain Extraction Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3 Modeled-based Level Set . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3 METHODS 17 3.1 Basic Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.1.1 Cluster Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.1.2 Parametric Active Contour . . . . . . . . . . . . . . . . . . . . . 20 3.2 Proposed methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2.1 Stage 1 : Image preprocessing . . . . . . . . . . . . . . . . . . . 26 3.2.2 Stage 2 : Image segmentation . . . . . . . . . . . . . . . . . . . 31 4 EXPERIMENTAL RESULTS 41 4.1 Data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.2 Quantitative analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 5 DISCUSSION AND CONCLUSIONS 50 REFERENCES 53 Appendix A List of symbols 60 Appendix B Snake evolution 63 | |
dc.language.iso | en | |
dc.title | 利用可適性彈性蛇變曲線模型從事核磁共振影像之腦部區域分割 | zh_TW |
dc.title | Skull-Stripping Brain MR Images Using an Adaptive Balloon Snake Model | en |
dc.type | Thesis | |
dc.date.schoolyear | 100-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 黃乾綱(Chien-Kang Huang),江明彰(Ming-Chang Chiang) | |
dc.subject.keyword | 去除頭骨,影像分割,主動輪廓模型,模糊可能性分群, | zh_TW |
dc.subject.keyword | Skull-stripping,image segmentation,active contour model,fuzzy possibilistic c-means, | en |
dc.relation.page | 66 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2012-08-14 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
顯示於系所單位: | 工程科學及海洋工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-101-1.pdf 目前未授權公開取用 | 2.34 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。