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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 鄭士康(Shyh-Kang Jeng) | |
dc.contributor.author | An-Cheng Chang | en |
dc.contributor.author | 張安政 | zh_TW |
dc.date.accessioned | 2021-05-15T17:55:38Z | - |
dc.date.available | 2017-02-03 | |
dc.date.available | 2021-05-15T17:55:38Z | - |
dc.date.copyright | 2015-02-03 | |
dc.date.issued | 2014 | |
dc.date.submitted | 2014-11-05 | |
dc.identifier.citation | [1] Global status report on noncommunicable diseases 2010. Geneva, World Health Organization, 2011.
[2] P. A. Heidenreich, J. G. Trogdon, O. A. Khavjou, J. Butler, K. Dracup, M. D. Ezekowitz, E. A. Finkelstein, Y. Hong, S. C. Johnston, A. Khera, D. M. Lloyd-Jones, S. A. Nelson, G. Nichol, D. Orenstein, P. W. F. Wilson, and Y. J. Woo, “Forecasting the Future of Cardiovascular Disease in the United States A Policy Statement From the American Heart Association,” Circulation, vol. 123, no. 8, pp. 933–944, Mar. 2011. [3] K. S. Reddy and S. Yusuf, “Emerging Epidemic of Cardiovascular Disease in Developing Countries,” Circulation, vol. 97, no. 6, pp. 596–601, Feb. 1998. [4] H.-Y. Lee, N. C. F. Codella, M. D. Cham, J. W. Weinsaft, and Y. Wang, “Automatic Left Ventricle Segmentation Using Iterative Thresholding and an Active Contour Model With Adaptation on Short-Axis Cardiac MRI,” IEEE Trans. Biomed. Eng., vol. 57, no. 4, pp. 905–913, Apr. 2010. [5] R. J. van der Geest, E. Jansen, V. G. M. Buller, and J. H. C. Reiber, “Automated detection of left ventricular epi- and endocardial contours in short-axis MR images,” in Computers in Cardiology 1994, 1994, pp. 33–36. [6] Y.-L. Lu, K. A. Connelly, A. J. Dick, G. A. Wright, and P. E. Radau, “Automatic functional analysis of left ventricle in cardiac cine MRI,” Quant. Imaging Med. Surg., vol. 3, no. 4, pp. 200–209, Aug. 2013. [7] C. Petitjean and J.-N. Dacher, “A review of segmentation methods in short axis cardiac MR images,” Med. Image Anal., vol. 15, no. 2, pp. 169–184, Apr. 2011. [8] C. A. Cocosco, W. J. Niessen, T. Netsch, E. P. A. Vonken, G. Lund, A. Stork, and M. A. Viergever, “Automatic image-driven segmentation of the ventricles in cardiac cine MRI,” J. Magn. Reson. Imaging, vol. 28, no. 2, pp. 366–374, Aug. 2008. [9] S. Xu, C. Pei, and H. Hu, “Endocardium and Epicardium Segmentation in MR Images Based on Developed Otsu and Dynamic Programming,” Sens. Transducers, Mar. 2014. [10] S. Huang, J. Liu, L. C. Lee, S. K. Venkatesh, L. L. S. Teo, C. Au, and W. L. Nowinski, “An Image-Based Comprehensive Approach for Automatic Segmentation of Left Ventricle from Cardiac Short Axis Cine MR Images,” J. Digit. Imaging, vol. 24, no. 4, pp. 598–608, Aug. 2011. [11] Y. Lu, P. Radau, K. Connelly, A. Dick, and G. Wright, “Automatic Image-Driven Segmentation of Left Ventricle in Cardiac Cine MRI,” MIDAS J. - Card. MR Left Ventricle Segmentation Chall., 2009. [12] S. Huang, J. Liu, L. C. Lee, S. K. Venkatesh, L. L. S. Teo, C. Au, and W. L. Nowinski, “Segmentation of the Left Ventricle from Cine MR Images Using a Comprehensive Approach,” MIDAS J. - Card. MR Left Ventricle Segmentation Chall., 2009. [13] J. Cousty, L. Najman, M. Couprie, S. Clement-Guinaudeau, T. Goissen, and J. Garot, “Segmentation of 4D cardiac MRI: Automated method based on spatio-temporal watershed cuts,” Image Vis. Comput., vol. 28, no. 8, pp. 1229–1243, Aug. 2010. [14] H. Hu, H. Liu, Z. Gao, and L. Huang, “Hybrid segmentation of left ventricle in cardiac MRI using Gaussian-mixture model and region restricted dynamic programming,” Magn. Reson. Imaging, vol. 31, no. 4, pp. 575–584, May 2013. [15] Nobuyuki Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. Syst. Man Cybern., vol. 9, no. 1, pp. 62–66, Jan. 1979. [16] C. Constantinides, E. Roullot, M. Lefort, and F. Frouin, “Fully automated segmentation of the left ventricle applied to cine MR images: Description and results on a database of 45 Subjects,” in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2012, pp. 3207–3210. [17] J. Wijnhout, D. Hendriksen, H. Van Assen, and R. Van der Geest, “LV Challenge LKEB Contribution: Fully Automated Myocardial Contour Detection,” MIDAS J. - Card. MR Left Ventricle Segmentation Chall., 2009. [18] I. Ben Ayed, S. Li, and I. Ross, “Embedding Overlap Priors in Variational Left Ventricle Tracking,” IEEE Trans. Med. Imaging, vol. 28, no. 12, pp. 1902–1913, Dec. 2009. [19] G. Tarroni, D. Marsili, F. Veronesi, C. Corsi, C. Lamberti, and G. Sanguinetti, “Near-automated 3D segmentation of left and right ventricles on magnetic resonance images,” in 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA), 2013, pp. 522–527. [20] M. Lynch, O. Ghita, and P. F. Whelan, “Segmentation of the Left Ventricle of the Heart in 3-D+t MRI Data Using an Optimized Nonrigid Temporal Model,” IEEE Trans. Med. Imaging, vol. 27, no. 2, pp. 195–203, Feb. 2008. [21] Q. C. Pham, F. Vincent, P. Clarysse, P. Croisille, and I. E. Magnin, “A FEM-based deformable model for the 3D segmentation and tracking of the heart in cardiac MRI,” in Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis, 2001. ISPA 2001, 2001, pp. 250–254. [22] J. Schaerer, C. Casta, J. Pousin, and P. Clarysse, “A dynamic elastic model for segmentation and tracking of the heart in MR image sequences,” Med. Image Anal., vol. 14, no. 6, pp. 738–749, Dec. 2010. [23] C. Casta, P. Clarysse, J. Schaerer, and J. Pousin, “Evaluation of the Dynamic Deformable Elastic Template model for the segmentation of the heart in MRI sequences,” MIDAS J. - Card. MR Left Ventricle Segmentation Chall., 2009. [24] M.-P. Jolly, C. Guetter, X. Lu, H. Xue, and J. Guehring, “Automatic Segmentation of the Myocardium in Cine MR Images Using Deformable Registration,” in Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges, O. Camara, E. Konukoglu, M. Pop, K. Rhode, M. Sermesant, and A. Young, Eds. Springer Berlin Heidelberg, 2012, pp. 98–108. [25] M. Lorenzo-Valdes, G. I. Sanchez-Ortiz, A. G. Elkington, R. H. Mohiaddin, and D. Rueckert, “Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm,” Med. Image Anal., vol. 8, no. 3, pp. 255–265, Sep. 2004. [26] V. Hartwig, G. Giovannetti, N. Vanello, M. Lombardi, L. Landini, and S. Simi, “Biological effects and safety in magnetic resonance imaging: a review,” Int. J. Environ. Res. Public. Health, vol. 6, no. 6, pp. 1778–1798, Jun. 2009. [27] A. O. Zurick, J. L. Klein, and M. S. Runge, Netter’s Cardiology, 2nd ed. Elsevier, 2010. [28] M.-P. Jolly, “Automatic Recovery of the Left Ventricular Blood Pool in Cardiac Cine MR Images,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008, D. Metaxas, L. Axel, G. Fichtinger, and G. Szekely, Eds. Springer Berlin Heidelberg, 2008, pp. 110–118. [29] C. Rhemann, A. Hosni, M. Bleyer, C. Rother, and M. Gelautz, “Fast cost-volume filtering for visual correspondence and beyond,” in 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011, pp. 3017–3024. [30] A. Pednekar, U. Kurkure, R. Muthupillai, S. Flamm, and I. Kakadiaris, “Automated left ventricular segmentation in cardiac MRI,” IEEE Trans. Biomed. Eng., vol. 53, no. 7, pp. 1425–1428, Jul. 2006. [31] K. He, J. Sun, and X. Tang, “Guided Image Filtering,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 6, pp. 1397–1409, Jun. 2013. [32] J. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-8, no. 6, pp. 679–698, Nov. 1986. [33] P. Radau, Y. Lu, K. Connelly, G. Paul, A. J. Dick, and G. A. Wright, “Evaluation Framework for Algorithms Segmenting Short Axis Cardiac MRI.,” MIDAS J. - Card. MR Left Ventricle Segmentation Chall., 2009. [34] M. Jolly, “Fully automatic left ventricle segmentation in cardiac cine MR images using registration and minimum surfaces,” MIDAS J.-Card. MR Left Ventricle Segmentation Chall., vol. 4, 2009. [35] C. Feng, C. Li, D. Zhao, C. Davatzikos, and H. Litt, “Segmentation of the Left Ventricle Using Distance Regularized Two-Layer Level Set Approach,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, K. Mori, I. Sakuma, Y. Sato, C. Barillot, and N. Navab, Eds. Springer Berlin Heidelberg, 2013, pp. 477–484. [36] T. A. Ngo and G. Carneiro, “Left ventricle segmentation from cardiac MRI combining level set methods with deep belief networks,” in 2013 20th IEEE International Conference on Image Processing (ICIP), 2013, pp. 695–699. [37] C. Constantinides, Y. Chenoune, N. Kachenoura, E. Roullot, E. Mousseaux, A. Herment, and F. Frouin, “Semi-automated cardiac segmentation on cine magnetic resonance images using GVF-Snake deformable models,” MIDAS J. - Card. MR Left Ventricle Segmentation Chall., 2009. [38] L. Cordero-Grande, G. Vegas-Sanchez-Ferrero, P. Casaseca-de-la-Higuera, J. Alberto San-Roman-Calvar, A. Revilla-Orodea, M. Martin-Fernandez, and C. Alberola-Lopez, “Unsupervised 4D myocardium segmentation with a Markov Random Field based deformable model,” Med. Image Anal., vol. 15, no. 3, pp. 283–301, Jun. 2011. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/5310 | - |
dc.description.abstract | 心血管疾病通常伴隨異常的心臟功能參數,舉凡過高或過低的左心室射出分率(ejection fraction)、心輸出量(cardiac output)的不足等。這些心臟功能參數可以從心臟磁振影像(CMR images)的掃描結果加以處理推估而得,其中重要環節即為影像分割技術。過去的自動左心室影像分割演算法的效能通常受限於複雜的心肌內壁結構,或者較為繁複的使用者操作。鑑此,本研究提出自動化的高精確度心臟磁振影像左心室分割演算法。為了克服磁振造影失真現象與心肌內壁不規則結構—如心肉柱及乳狀肌—所造成的影像分割難度,其結合了針對心臟磁振影像調校的cost-volume filtering技術與新創的心肌輪廓擷取演算法以達到此目的。實驗結果顯示切割精準度和可靠性皆優於先前方法,並因此減少校正所需時間,自動導出的心臟功能參數與人工計算結果則呈高度相關性。各項數據顯示本研究所提出的心臟磁振影像左心室分割演算法是現今效能最好的演算法之一。 | zh_TW |
dc.description.abstract | Cardiovascular diseases are often associated with abnormal left ventricular (LV) cardiac parameters, such as deviation of ejection fraction (EF) and cardiac output. These information can be extracted from cardiac magnetic resonance (CMR) scans of the heart, which involves image segmentation in CMR images. Previous works on left ventricle segmentation in CMR images are often hindered by complex inner heart wall geometry or they require a more involved operator intervention. In this work, we employ novel cost-volume filtering (CVF) scheme combined with novel myocardial contour processing framework to overcome the segmentation difficulty resulted from MR imaging artifacts and inner heart wall irregularities (e.g., papillary muscle and trabeculae carneae). Result shows improved accuracy and robustness over previous works. In clinical aspects, quantitative analysis shows close agreement between manually and automatically determined cardiac functions with no systematic bias in EF estimation error. | en |
dc.description.provenance | Made available in DSpace on 2021-05-15T17:55:38Z (GMT). No. of bitstreams: 1 ntu-103-R01942108-1.pdf: 5711452 bytes, checksum: 57760d58ca75c87d6d2a50882eb32d35 (MD5) Previous issue date: 2014 | en |
dc.description.tableofcontents | 口試委員會審定書 #
ACKNOWLEDMENTS i 摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES viii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Research Goal 2 1.3 Contribution 4 1.4 Literature Survey 4 1.5 Thesis Outline 6 Chapter 2 Fundamentals 7 2.1 Cardiac Magnetic Resonance Imaging 7 2.2 Ventricles in Cardiac MR Short-Axis View 7 2.3 Assessing Left Ventricular Functions 11 Chapter 3 Proposed Approach 14 3.1 System Overview 14 3.2 Left Ventricle Localization 16 3.2.1 Detection of left ventricular cavity 16 3.2.2 Iterative ROI refinement 16 3.3 Segmenting LV Blood Pool by Cost-Volume Filtering 19 3.3.1 Cost-volume-filtering-based image segmentation 19 3.3.2 Polar coordinates mapping 21 3.3.3 Cost-volume initialization. 21 3.3.4 Cost-volume filtering 23 3.4 Endocardial Contour Processing 25 3.4.1 Generating contour from detected LV blood pool 25 3.4.2 Complementary contour generation 29 3.4.3 Endocardial contour regularization 31 3.5 Cardiac Cycle Determination 33 Chapter 4 Results and Discussion 34 4.1 Test Dataset and Evaluation Metrics 34 4.2 Evaluating Segmentation Results 36 4.3 Evaluating Left Ventricular Functions 40 4.4 Discussion 44 Chapter 5 Conclusion 51 REFERENCE 53 | |
dc.language.iso | en | |
dc.title | 使用CVF與新創輪廓擷取演算法於自動心臟磁振影像左心室分割 | zh_TW |
dc.title | Automated Left Ventricle Segmentation in Cardiac Short-Axis MR Images Using Cost-Volume Filtering and Novel Myocardial Contour Processing Framework | en |
dc.type | Thesis | |
dc.date.schoolyear | 103-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 貝蘇章(Soo-Chang Pei),鍾孝文(Hsiao-Wen Chung),丁建均(Jian-Jiun Ding),黃騰毅(Teng-Yi Huang) | |
dc.subject.keyword | cardiac,MRI,medical,image,segmentation, | zh_TW |
dc.subject.keyword | 心臟,磁振造影,醫療,影像,分割, | en |
dc.relation.page | 57 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2014-11-05 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
顯示於系所單位: | 電信工程學研究所 |
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