請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/60664
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張瑞峰(Ruey-Feng Chang) | |
dc.contributor.author | Yi-Ting Lu | en |
dc.contributor.author | 呂宜庭 | zh_TW |
dc.date.accessioned | 2021-06-16T10:25:14Z | - |
dc.date.available | 2018-08-20 | |
dc.date.copyright | 2013-08-20 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2013-08-15 | |
dc.identifier.citation | References
[1] R. Siegel, D. Naishadham, and A. Jemal, 'Cancer statistics, 2013,' CA: a cancer journal for clinicians, vol. 63, pp. 11-30, 2013. [2] E. Warner, D. B. Plewes, R. S. Shumak, G. C. Catzavelos, L. S. Di Prospero, M. J. Yaffe, V. Goel, E. Ramsay, P. L. Chart, D. E. C. Cole, G. A. Taylor, M. Cutrara, T. H. Samuels, J. P. Murphy, J. M. Murphy, and S. A. Narod, 'Comparison of breast magnetic resonance imaging, mammography, and ultrasound for surveillance of women at high risk for hereditary breast cancer,' Journal of Clinical Oncology, vol. 19, pp. 3524-3531, Aug 1 2001. [3] C. K. Kuhl, S. Schrading, H. B. Bieling, E. Wardelmann, C. C. Leutner, R. Koenig, W. Kuhn, and H. H. Schild, 'MRI for diagnosis of pure ductal carcinoma in situ: a prospective observational study,' Lancet, vol. 370, pp. 485-492, Aug 11 2007. [4] C. D. Lehman, J. D. Blume, D. Thickman, D. A. Bluemke, E. Pisano, C. Kuhl, T. B. Julian, N. Hylton, P. Weatherall, and M. O'Loughlin, 'Added cancer yield of MRI in screening the contralateral breast of women recently diagnosed with breast cancer: results from the International Breast Magnetic Resonance Consortium (IBMC) trial,' Journal of surgical oncology, vol. 92, pp. 9-15, 2005. [5] P. Kozlowski, S. D. Chang, E. C. Jones, K. W. Berean, H. Chen, and S. L. Goldenberg, 'Combined diffusion‐weighted and dynamic contrast‐enhanced MRI for prostate cancer diagnosis—Correlation with biopsy and histopathology,' Journal of Magnetic Resonance Imaging, vol. 24, pp. 108-113, 2006. [6] W. Chen, M. L. Giger, U. Bick, and G. M. Newstead, 'Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI,' Medical Physics, vol. 33, p. 2878, 2006. [7] W. J. Chen, M. L. Giger, L. Lan, and U. Bick, 'Computerized interpretation of breast MRI: Investigation of enhancement-variance dynamics,' Medical Physics, vol. 31, pp. 1076-1082, May 2004. [8] D. Lebihan, E. Breton, D. Lallemand, P. Grenier, E. Cabanis, and M. Lavaljeantet, 'Mr Imaging of Intravoxel Incoherent Motions - Application to Diffusion and Perfusion in Neurologic Disorders,' Radiology, vol. 161, pp. 401-407, Nov 1986. [9] H. A. Kramers, 'Brownian motion in a field of force and the diffusion model of chemical reactions,' Physica, vol. 7, pp. 284-304, 1940. [10] Y. Paran, P. Bendel, R. Margalit, and H. Degani, 'Water diffusion in the different microenvironments of breast cancer,' NMR in Biomedicine, vol. 17, pp. 170-180, 2004. [11] S. C. Partridge, C. D. Mullins, B. F. Kurland, M. D. Allain, W. B. DeMartini, P. R. Eby, and C. D. Lehman, 'Apparent diffusion coefficient values for discriminating benign and malignant breast MRI lesions: effects of lesion type and size,' AJR Am J Roentgenol, vol. 194, pp. 1664-73, Jun 2010. [12] T. E. Yankeelov, M. Lepage, A. Chakravarthy, E. E. Broome, K. J. Niermann, M. C. Kelley, I. Meszoely, I. A. Mayer, C. R. Herman, and K. McManus, 'Integration of quantitative DCE-MRI and ADC mapping to monitor treatment response in human breast cancer: initial results,' Magnetic resonance imaging, vol. 25, pp. 1-13, 2007. [13] B. Turkbey, D. Thomasson, Y. X. Pang, M. Bernardo, and P. L. Choyke, 'The role of dynamic contrast-enhanced MRI in cancer diagnosis and treatment,' Diagnostic and Interventional Radiology, vol. 16, pp. 186-192, Sep 2010. [14] P. Hayton, M. Brady, L. Tarassenko, and N. Moore, 'Analysis of dynamic MR breast images using a model of contrast enhancement,' Medical Image Analysis, vol. 1, pp. 207-224, 1997. [15] Y. C. Chang, Y. H. Huang, C. S. Huang, and R. F. Chang, 'Vascular Morphology and Tortuosity Analysis of Breast Tumor inside and Outside Contour by 3-D Power Doppler Ultrasound,' Ultrasound in Medicine and Biology, vol. 38, pp. 1859-1869, Nov 2012. [16] K. Holli, A. L. Laaperi, L. Harrison, T. Luukkaala, T. Toivonen, P. Ryymin, P. Dastidar, S. Soimakallio, and H. Eskola, 'Characterization of Breast Cancer Types by Texture Analysis of Magnetic Resonance Images,' Academic Radiology, vol. 17, pp. 135-141, Feb 2010. [17] A. Karahaliou, K. Vassiou, S. Skiadopoulos, T. Kanavou, A. Yiakoumelos, and L. Costaridou, 'Quantifying heterogeneity of lesion uptake in dynamic contrast enhanced MRI for breast cancer diagnosis,' Journal of Instrumentation, vol. 4, Jul 2009. [18] K. Nie, J. H. Chen, H. J. Yu, Y. Chu, O. Nalcioglu, and M. Y. Su, 'Quantitative Analysis of Lesion Morphology and Texture Features for Diagnostic Prediction in Breast MRI,' Academic Radiology, vol. 15, pp. 1513-1525, Dec 2008. [19] D. Newell, K. Nie, J. H. Chen, C. C. Hsu, H. J. Yu, O. Nalcioglu, and M. Y. Su, 'Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: differences in lesions presenting as mass and non-mass-like enhancement,' European Radiology, vol. 20, pp. 771-781, Apr 2010. [20] W. Chen, M. L. Giger, H. Li, U. Bick, and G. M. Newstead, 'Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images,' Magnetic Resonance in Medicine, vol. 58, pp. 562-571, Sep 2007. [21] E. Morris and L. Liberman, Breast Magnetic Resonance Imaging vol. 255: Springer, 2005. [22] W. K. Moon, Y. W. Shen, C. S. Huang, L. R. Chiang, and R. F. Chang, 'Computer-Aided Diagnosis for the Classification of Breast Masses in Automated Whole Breast Ultrasound Images,' Ultrasound in Medicine and Biology, vol. 37, pp. 539-548, Apr 2011. [23] T. Yoshizako, A. Wada, T. Hayashi, K. Uchida, M. Sumura, N. Uchida, H. Kitagaki, and M. Igawa, 'Usefulness of diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging in the diagnosis of prostate transition-zone cancer,' Acta Radiologica, vol. 49, pp. 1207-1213, 2008. [24] S. Kul, A. Cansu, E. Alhan, H. Dinc, G. Gunes, and A. Reis, 'Contribution of diffusion-weighted imaging to dynamic contrast-enhanced MRI in the characterization of breast tumors,' American Journal of Roentgenology, vol. 196, pp. 210-217, 2011. [25] M. Han, T. H. Kim, D. K. Kang, K. S. Kim, and H. Yim, 'Prognostic Role of MRI Enhancement Features in Patients With Breast Cancer: Value of Adjacent Vessel Sign and Increased Ipsilateral Whole-Breast Vascularity,' American Journal of Roentgenology, vol. 199, pp. 921-928, Oct 2012. [26] D. R. Fischer, A. Malich, S. Wurdinger, J. Boettcher, M. Dietzel, and W. A. Kaiser, 'The adjacent vessel on dynamic contrast-enhanced breast MRI,' American Journal of Roentgenology, vol. 187, pp. W147-W151, 2006. [27] M. Dietzel, P. A. T. Baltzer, T. Vag, A. Herzog, M. Gajda, O. Camara, and W. A. Kaiser, 'The Adjacent Vessel Sign on Breast MRL New Data and a Subgroup Analysis for 1,084 Histologically Verified Cases,' Korean Journal of Radiology, vol. 11, pp. 178-186, Mar-Apr 2010. [28] S. Kul, A. Cansu, E. Alhan, H. Dinc, A. Reis, and G. Can, 'Contrast-enhanced MR angiography of the breast: evaluation of ipsilateral increased vascularity and adjacent vessel sign in the characterization of breast lesions,' American Journal of Roentgenology, vol. 195, pp. 1250-1254, 2010. [29] P. Belhomme, A. Elmoataz, P. Herlin, and D. Bloyet, 'Generalized region growing operator with optimal scanning: Application to segmentation of breast cancer images,' Journal of Microscopy-Oxford, vol. 186, pp. 41-50, Apr 1997. [30] R. M. Haralick, S. R. Sternberg, and X. Zhuang, 'Image analysis using mathematical morphology,' Pattern Analysis and Machine Intelligence, IEEE Transactions on, pp. 532-550, 1987. [31] Y. Sato, S. Nakajima, H. Atsumi, T. Koller, G. Gerig, S. Yoshida, and R. Kikinis, '3D multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,' Cvrmed-Mrcas'97, vol. 1205, pp. 213-222, 1997. [32] L. A. Meinel, A. H. Stolpen, K. S. Berbaum, L. L. Fajardo, and J. M. Reinhardt, 'Breast MRI lesion classification: Improved performance of human readers with a backpropagation neural network computer-aided diagnosis (CAD) system,' Journal of Magnetic Resonance Imaging, vol. 25, pp. 89-95, Jan 2007. [33] K. F. Mulchrone and K. R. Choudhury, 'Fitting an ellipse to an arbitrary shape: implications for strain analysis,' Journal of Structural Geology, vol. 26, pp. 143-153, 2004. [34] Z. Quiming and P. Lay-Kheng, 'A transformation-invariant recursive subdivision method for shape analysis,' in Pattern Recognition, 1988., 9th International Conference on, 1988, pp. 833-835. [35] L. G. Brown, 'A Survey of Image Registration Techniques,' Computing Surveys, vol. 24, pp. 325-376, Dec 1992. [36] B. G. Kashef and A. A. Sawchuk, 'A Survey of New Techniques for Image Registration and Mapping,' Proceedings of the Society of Photo-Optical Instrumentation Engineers, vol. 432, pp. 222-239, 1983. [37] R. El-Khouli, M. Jacobs, K. Macura, P. Barker, and D. Bluemke, 'Diffusion-Weighted Imaging and Apparent Diffusion Coefficients Mapping for Characterization of Focal Breast Lesions at 3T,' American Journal of Roentgenology, vol. 192, May 2009. [38] C. Marini, C. Iacconi, M. Giannelli, A. Cilotti, M. Moretti, and C. Bartolozzi, 'Quantitative diffusion-weighted MR imaging in the differential diagnosis of breast lesion,' European Radiology, vol. 17, pp. 2646-2655, Oct 2007. [39] R. F. Chang, S. F. Huang, W. K. Moon, Y. H. Lee, and D. R. Chen, 'Solid breast masses: Neural network analysis of vascular features at three-dimensional power Doppler US for benign or malignant classification,' Radiology, vol. 243, pp. 56-62, Apr 2007. [40] S. F. Huang, R. F. Chang, W. K. Moon, Y. H. Lee, D. R. Chen, and J. S. Suri, 'Analysis of tumor vascularity using three-dimensional power Doppler ultrasound images,' Ieee Transactions on Medical Imaging, vol. 27, pp. 320-330, Mar 2008. [41] K. P. Dhamanaskar, D. Muradali, S. R. Kulkarni, K. Bukhanov, S. C. Pantazi, and C. Wilson, 'MRI directed ultrasound: A cost effective method for diagnosis and intervention in breast imaging,' Radiology, vol. 225, pp. 653-653, Nov 2002. [42] Y.-H. Huang, J.-H. Chen, Y.-C. Chang, C.-S. Huang, W. K. Moon, W.-J. Kuo, K.-J. Lai, and R.-F. Chang, 'Diagnosis of Solid Breast Tumors Using Vessel Analysis in Three-Dimensional Power Doppler Ultrasound Images,' Journal of Digital Imaging, pp. 1-9, 2013. [43] A. Vagin and A. Teplyakov, 'Molecular replacement with MOLREP,' Acta Crystallographica Section D: Biological Crystallography, vol. 66, pp. 22-25, 2009. [44] R. M. Haralick, Shanmuga.K, and I. Dinstein, 'Textural Features for Image Classification,' Ieee Transactions on Systems Man and Cybernetics, vol. Smc3, pp. 610-621, 1973. [45] R. Woodhams, K. Matsunaga, K. Iwabuchi, S. Kan, H. Hata, M. Kuranami, M. Watanabe, and K. Hayakawa, 'Diffusion-weighted imaging of malignant breast tumors - The usefulness of apparent diffusion coefficient (ADC) value and ADC map for the detection of malignant breast tumors and evaluation of cancer extension,' Journal of Computer Assisted Tomography, vol. 29, pp. 644-649, Sep-Oct 2005. [46] J.-P. Galons, M. I. Altbach, G. D. Paine-Murrieta, C. W. Taylor, and R. J. Gillies, 'Early increases in breast tumor xenograft water mobility in response to paclitaxel therapy detected by non-invasive diffusion magnetic resonance imaging,' Neoplasia (New York, NY), vol. 1, p. 113, 1999. [47] N. A. Mohamed, M. Ahmed, and A. Farag, 'Modified fuzzy c-mean in medical image segmentation,' in Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on, 1999, pp. 3429-3432. [48] R. Stefanelli and A. Rosenfeld, 'Some parallel thinning algorithms for digital pictures,' Journal of the ACM (JACM), vol. 18, pp. 255-264, 1971. [49] R. F. Chang, S. F. Huang, W. K. Moon, Y. H. Lee, and D. R. Chen, 'Computer algorithm for analysing breast tumor angiogenesis using 3-D power doppler ultrasound,' Ultrasound in Medicine and Biology, vol. 32, pp. 1499-1508, Oct 2006. [50] S.-F. Huang, C. Ruey-Feng, W. K. Moon, Y.-H. Lee, C. Dar-Ren, and J. S. Suri, 'Analysis of tumor vascularity using three-dimensional power Doppler ultrasound images,' Medical Imaging, IEEE Transactions on, vol. 27, pp. 320-330, 2008. [51] A. Field, Discovering statistics using SPSS: Sage publications, 2009. [52] Y. Xiao, J. Hua, and E. R. Dougherty, 'Quantification of the impact of feature selection on the variance of cross-validation error estimation,' EURASIP Journal on Bioinformatics and Systems Biology, vol. 2007, pp. 1-1, 2007. [53] K. Y. Chan, C. Kwong, T. S. Dillon, and Y. Tsim, 'Reducing overfitting in manufacturing process modeling using a backward elimination based genetic programming,' Applied Soft Computing, vol. 11, pp. 1648-1656, 2011. [54] R. Kohavi and G. H. John, 'Wrappers for feature subset selection,' Artificial Intelligence, vol. 97, pp. 273-324, Dec 1997. [55] J. Folkman and M. Klagsbrun, 'Angiogenic factors,' Science, vol. 235, pp. 442-447, 1987. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/60664 | - |
dc.description.abstract | 近年的研究顯示,新生血管的癥狀在乳癌的預後中扮演著重要的角色。另外,鄰近乳房腫瘤血管的巨增也常被用來檢測惡性腫瘤的一個現象。因此,鄰近腫瘤周圍的血管能夠提供有效的資訊並且使用在乳癌良惡性診斷之中。本篇論文藉由電腦的輔助去綜合分析動態對比增強的核磁共振成像 (DCE-MRI)以及擴散加權影像 (DWI)所提供的一些腫瘤內部常用特徵,並且提出一個量化的方法去檢測腫瘤周圍的血管,進一步地去評估腫瘤周圍的血管資訊是否能提供更好的診斷結果。本篇論文實驗包含50個良性及54個惡性的乳癌腫瘤病例,根據實驗結果顯示,結合腫瘤外部的鄰近血管以及其他常用的腫瘤內部特徵能有效地區分腫瘤的良惡性並且提高診斷的結果,能達到準確性95.2%、敏感性96.3%、專一性94.0%以及Az值0.95。 | zh_TW |
dc.description.abstract | Angiogenesis sign plays a crucial role in breast tumor prognosis and the increased vascularity adjacent to breast tumor is reported to be useful in the malignancy evaluation. In this study, a quantitative method was proposed to extract the vessel information surrounding the tumor area in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). First, a ring-shaped region encompassed a tumor was segmented. The adjacent vessels were then extracted from the ring-shaped region. The quantitative vessel features were combined with the shape, texture, and apparent diffusion coefficient (ADC) extracted from the tumor area to improve the diagnostic performance of the proposed computer-aided (CAD) system. The collected cases used in the experiment included 50 benign and 54 malignant cases. As a result, the proposed CAD system using the combination of all features achieved the accuracy, sensitivity, specificity, and Az value of 95.2% (99/104), 96.3% (52/54), 94.0% (47/50), and 0.95, respectively. The accuracy was significantly better than that of individual kind of features (p-value<0.05). In summary, the proposed CAD system based on the quantitative vessel features would be promising in the diagnosis of breast cancer. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T10:25:14Z (GMT). No. of bitstreams: 1 ntu-102-R00944040-1.pdf: 1789323 bytes, checksum: e8b2fb558590f409dd7207b4725ad1a3 (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | 口試委員審定書 i
致謝 ii 摘要 iii Abstract iv Tabel of Contents v List of Figures vi List of Tables vii Chapter 1 Introduction 1 Chapter 2 Materials 3 2.1 Patients and Lesion Characters 3 2.2 Data Acquisition 3 Chapter 3 The Proposed DCE-MRI and DWI Tumor Diagnosis Method 6 3.1 Tumor Segmentation 8 3.2 Ring-shaped Region Extraction 8 3.3 Registration 11 3.4 Features Extraction 13 3.4.1 Texture Features 13 3.4.2 Shape Features 15 3.4.3 ADC Features 18 3.4.4 Vessel Features 20 3.4.4.1 Vessel Morphological Features 23 3.4.4.2 Vessel Tortuous Features 24 Chapter 4 Experiments and Results 29 4.1 Classification 29 4.2 Statistics Analysis 30 4.3 Experimental Results 31 4.4 Discussion 39 Chapter 5 Conclusion and Future Work 43 References 46 | |
dc.language.iso | en | |
dc.title | 以乳房動態對比增強核磁影像之血管分析與擴散加權核磁影像之表面擴散係數基礎的乳癌腫瘤輔助診斷 | zh_TW |
dc.title | Computer-aided Diagnosis of Breast Cancer Based on Vascular Analysis of DCE-MRI and Apparent Diffusion Coefficient of DWI | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 黃俊升,張允中 | |
dc.subject.keyword | 動態對比增強核磁共振影像,擴散加權核磁共振影像,乳房,血管,表面擴散係數, | zh_TW |
dc.subject.keyword | DCE-MRI,DWI,breast,Vessel,ADC, | en |
dc.relation.page | 51 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2013-08-15 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
顯示於系所單位: | 資訊網路與多媒體研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-102-1.pdf 目前未授權公開取用 | 1.75 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。