請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/61191完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張瑞峰 | |
| dc.contributor.author | Ming-Jen Hung | en |
| dc.contributor.author | 洪明仁 | zh_TW |
| dc.date.accessioned | 2021-06-16T10:52:06Z | - |
| dc.date.available | 2018-08-28 | |
| dc.date.copyright | 2013-08-28 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-08-09 | |
| dc.identifier.citation | Reference
[1] R. Siegel, D. Naishadham, and A. Jemal, 'Cancer statistics, 2013,' CA: a cancer journal for clinicians, vol. 63, pp. 11-30, 2013. [2] A. Jemal, F. Bray, M. M. Center, J. Ferlay, E. Ward, and D. Forman, 'Global cancer statistics,' CA: a cancer journal for clinicians, vol. 61, pp. 69-90, 2011. [3] E. A. Sickles, 'Breast Imaging: From 1965 to the Present1,' Radiology, vol. 215, pp. 1-16, 2000. [4] M. A. Roubidoux, M. A. Helvie, N. E. Lai, and C. Paramagul, 'Bilateral breast cancer: early detection with mammography,' Radiology, vol. 196, pp. 427-431, 1995. [5] T. E. Wilson, M. A. Helvie, and D. A. August, 'Breast cancer in the elderly patient: early detection with mammography,' Radiology, vol. 190, pp. 203-207, 1994. [6] E. A. Sickles, R. Filly, and P. Callen, 'Benign breast lesions: ultrasound detection and diagnosis,' Radiology, vol. 151, pp. 467-470, 1984. [7] T. M. Kolb, J. Lichy, and J. H. Newhouse, 'Comparison of the performance of screening mammography, physical examination, and breast us and evaluation of factors that influence them: an analysis of 27,825 patient evaluations1,' Radiology, vol. 225, pp. 165-175, 2002. [8] K. M. Kelly, J. Dean, W. S. Comulada, and S.-J. Lee, 'Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts,' European radiology, vol. 20, pp. 734-742, 2010. [9] N. F. Boyd, G. S. Dite, J. Stone, A. Gunasekara, D. R. English, M. R. McCredie, et al., 'Heritability of mammographic density, a risk factor for breast cancer,' New England Journal of Medicine, vol. 347, pp. 886-894, 2002. [10] P. Crystal, S. D. Strano, S. Shcharynski, and M. J. Koretz, 'Using sonography to screen women with mammographically dense breasts,' American Journal of Roentgenology, vol. 181, pp. 177-182, 2003. [11] W. K. Moon, D.-Y. Noh, and J.-G. Im, 'Multifocal, Multicentric, and Contralateral Breast Cancers: Bilateral Whole-Breast US in the Preoperative Evaluation of Patients1,' Radiology, vol. 224, pp. 569-576, 2002. [12] K. Flobbe, P. Nelemans, A. Kessels, G. Beets, M. Von Meyenfeldt, and J. Van Engelshoven, 'The role of ultrasonography as an adjunct to mammography in the detection of breast cancer: a systematic review,' European Journal of Cancer, vol. 38, pp. 1044-1050, 2002. [13] K. J. Taylor, C. Merritt, C. Piccoli, R. Schmidt, G. Rouse, B. Fornage, et al., 'Ultrasound as a complement to mammography and breast examination to characterize breast masses,' Ultrasound in medicine & biology, vol. 28, pp. 19-26, 2002. [14] E. Wenkel, M. Heckmann, M. Heinrich, S. Schwab, M. Uder, R. Schulz-Wendtland, et al., 'Automated Breast Ultrasound: Lesion Detection and BI-RAD Classification-a Pilot Study,' Rofo, vol. 180, pp. 804-808, 2008. [15] Y.-H. Chou, C.-M. Tiu, J. Chen, and R.-F. Chang, 'Automated full-field breast ultrasonography: the past and the present,' Journal of Medical Ultrasound, vol. 15, pp. 31-44, 2007. [16] R.-F. Chang, K.-C. Chang-Chien, H.-J. Chen, D.-R. Chen, E. Takada, and W. Kyung Moon, 'Whole breast computer-aided screening using free-hand ultrasound,' in International Congress Series, 2005, pp. 1075-1080. [17] W. Buchberger, A. Niehoff, P. Obrist, P. DeKoekkoek-Doll, and M. Dunser, 'Clinically and mammographically occult breast lesions: detection and classification with high-resolution sonography,' in Seminars in Ultrasound, CT and MRI, 2000, pp. 325-336. [18] P. B. Gordon and S. L. Goldenberg, 'Malignant breast masses detected only by ultrasound. A retrospective review,' Cancer, vol. 76, pp. 626-630, 1995. [19] R.-F. Chang, K.-C. Chang-Chien, E. Takada, C.-S. Huang, Y.-H. Chou, C.-M. Kuo, et al., 'Rapid image stitching and computer-aided detection for multipass automated breast ultrasound,' Medical physics, vol. 37, p. 2063, 2010. [20] K. Drukker, M. L. Giger, K. Horsch, M. A. Kupinski, C. J. Vyborny, and E. B. Mendelson, 'Computerized lesion detection on breast ultrasound,' Medical Physics, vol. 29, p. 1438, 2002. [21] K. Drukker, M. L. Giger, and E. B. Mendelson, 'Computerized analysis of shadowing on breast ultrasound for improved lesion detection,' Medical physics, vol. 30, p. 1833, 2003. [22] K. Drukker, M. L. Giger, and C. E. Metz, 'Robustness of Computerized Lesion Detection and Classification Scheme across Different Breast US Platforms1,' Radiology, vol. 237, pp. 834-840, 2005. [23] Y. Ikedo, D. Fukuoka, T. Hara, H. Fujita, E. Takada, T. Endo, et al., 'Development of a fully automatic scheme for detection of masses in whole breast ultrasound images,' Medical physics, vol. 34, p. 4378, 2007. [24] W. Moon, Y. Shen, M. Bae, C. Huang, J. Chen, and R. Chang, 'Computer-Aided Tumor Detection Based on Multi-Scale Blob Detection Algorithm in Automated Breast Ultrasound Images,' Medical Imaging, IEEE Transactions on, 2012. [25] D. P. Chakraborty, 'Maximum likelihood analysis of free-response receiver operating characteristic (FROC) data,' Medical physics, vol. 16, pp. 561-568, 1989. [26] S. Yu and L. Guan, 'A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films,' Medical Imaging, IEEE Transactions on, vol. 19, pp. 115-126, 2000. [27] D. Chakraborty, E. Breatnach, M. Yester, B. Soto, G. Barnes, and R. Fraser, 'Digital and conventional chest imaging: a modified ROC study of observer performance using simulated nodules,' Radiology, vol. 158, pp. 35-39, 1986. [28] E. N. Mortensen and W. A. Barrett, 'Toboggan-based intelligent scissors with a four-parameter edge model,' in Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on., 1999. [29] R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital image processing using MATLAB vol. 2: Gatesmark Publishing Tennessee, 2009. [30] C. Beigelman-Aubry, C. Hill, A. Guibal, J. Savatovsky, and P. A. Grenier, 'Multi–Detector Row CT and Postprocessing Techniques in the Assessment of Diffuse Lung Disease1,' Radiographics, vol. 25, pp. 1639-1652, 2005. [31] T. Tuthill, R. Sperry, and K. Parker, 'Deviations from Rayleigh statistics in ultrasonic speckle,' Ultrasonic imaging, vol. 10, pp. 81-89, 1988. [32] P. Perona and J. Malik, 'Scale-space and edge detection using anisotropic diffusion,' Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 12, pp. 629-639, 1990. [33] A. P. Mangan and R. T. Whitaker, 'Partitioning 3D surface meshes using watershed segmentation,' Visualization and Computer Graphics, IEEE Transactions on, vol. 5, pp. 308-321, 1999. [34] A. Rosenfeld and J. L. Pfaltz, 'Sequential operations in digital picture processing,' Journal of the ACM (JACM), vol. 13, pp. 471-494, 1966. [35] M.-C. Yang, C.-S. Huang, J.-H. Chen, and R.-F. Chang, 'Whole Breast Lesion Detection Using Naive Bayes Classifier for Portable Ultrasound,' Ultrasound in medicine & biology, 2012. [36] E. Bribiesca, 'An easy measure of compactness for 2D and 3D shapes,' Pattern Recognition, vol. 41, pp. 543-554, 2008. [37] R. M. Haralick, K. Shanmugam, and I. H. Dinstein, 'Textural features for image classification,' Systems, Man and Cybernetics, IEEE Transactions on, pp. 610-621, 1973. [38] W.-C. Shen, R.-F. Chang, W. K. Moon, Y.-H. Chou, and C.-S. Huang, 'Breast ultrasound computer-aided diagnosis using BI-RADS features,' Academic radiology, vol. 14, p. 928, 2007. [39] M. Tuceryan and A. K. Jain, 'Texture analysis,' Handbook of pattern recognition and computer vision, vol. 276, 1993. [40] A. Baraldi and F. Parmiggiani, 'An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters,' Geoscience and Remote Sensing, IEEE Transactions on, vol. 33, pp. 293-304, 1995. [41] W. K. Moon, C.-M. Lo, C.-S. Huang, J.-H. Chen, and R.-F. Chang, 'Computer-aided diagnosis based on speckle patterns in ultrasound images,' Ultrasound in medicine & biology, 2012. [42] W. K. Moon, C.-M. Lo, J. M. Chang, C.-S. Huang, J.-H. Chen, and R.-F. Chang, 'Computer-aided classification of breast masses using speckle features of automated breast ultrasound images,' Medical physics, vol. 39, p. 6465, 2012. [43] A. Field, Discovering statistics using SPSS: Sage Publications Limited, 2005. [44] D. W. Hosmer and S. Lemeshow, Applied logistic regression vol. 354: Wiley-Interscience, 2000. [45] S. Holm, 'A simple sequentially rejective multiple test procedure,' Scandinavian journal of statistics, vol. 6, pp. 65-70, 1979. [46] R. C. Sprinthall and S. T. Fisk, Basic statistical analysis: Prentice Hall Englewood Cliffs, NJ, 1990. [47] S. W. Chan, P. S. Cheung, S. Chan, S. S. Lau, T. T. Wong, M. Ma, et al., 'Benefit of ultrasonography in the detection of clinically and mammographically occult breast cancer,' World journal of surgery, vol. 32, pp. 2593-2598, 2008. [48] S. S. Kaplan, 'Clinical Utility of Bilateral Whole-Breast US in the Evaluation of Women with Dense Breast Tissue1,' Radiology, vol. 221, pp. 641-649, 2001. [49] V. Corsetti, A. Ferrari, M. Ghirardi, R. Bergonzini, S. Bellarosa, O. Angelini, et al., 'Role of ultrasonography in detecting mammographically occult breast carcinoma in women with dense breasts,' La radiologia medica, vol. 111, pp. 440-448, 2006. [50] K. Uchida, A. Yamashita, K. Kawase, and K. Kamiya, 'Screening ultrasonography revealed 15% of mammographically occult breast cancers,' Breast Cancer, vol. 15, pp. 165-168, 2008. [51] S. Theodoridis and K. Koutroumbas, 'Pattern Recognition,' ed: Academic Press, Elsevier, USA. [52] J. Canny, 'A computational approach to edge detection,' Pattern Analysis and Machine Intelligence, IEEE Transactions on, pp. 679-698, 1986. [53] I. Hacihaliloglu, R. Abugharbieh, A. J. Hodgson, and R. N. Rohling, 'Automatic Adaptive Parameterization in Local Phase Feature-Based Bone Segmentation in Ultrasound,' Ultrasound in medicine & biology, vol. 37, pp. 1689-1703, 2011. [54] T. Tan, B. Platel, R. Mann, H. Huisman, and N. Karssemeijer, 'Chest wall segmentation in automated 3D breast ultrasound scans,' Medical image analysis, 2012. [55] Y. Zhao, 'Lattice Boltzmann based PDE solver on the GPU,' The Visual Computer, vol. 24, pp. 323-333, 2008. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/61191 | - |
| dc.description.abstract | 全乳房自動超音波是一種熱門的檢測乳房腫瘤的儀器。相較於傳統的手持式超音波，全乳房自動超音波可以達到不依賴操作者的經驗以及用在大規模篩檢的可行性。然而逐張檢查數百片的全乳房自動超音波影像需要花費很多的時間，本論文提出一個基於分水嶺切割的電腦輔助乳房腫瘤偵測系統來加速檢查影像的時間。分水嶺切割會將影像中每一個局部極小灰階值附近相似的組織合併成為一個同質性的區域。利用每一個區域中量化的形態學、灰階值以及材質特徵來計算出此區域被視為腫瘤的可能性，而這三大類的特徵也應用於多維度的偽陽性篩除，來減少非腫瘤區域被誤判成腫瘤的情形發生。實驗中所用到的測試資料包含了68個良性以及65個惡性腫瘤。經由實驗結果，本系統在腫瘤偵測率100% (133/133)，90% (121/133) 以及80% (107/133) 的情況下，平均每一個全乳房自動超音波掃瞄會有9.44，5.42以及3.33個非腫瘤區域被誤判成腫瘤。另外，當系統同時使用三大類的特徵時，品質因數 (FOM) 可以達到0.46，這和使用其他特徵的組合相比時有顯著性差異(p-value<0.05)。總結，本論文所提出的基於分水嶺切割的電腦輔助乳房腫瘤偵測系統可利用多維度的偽陽性篩除有效地偵測在全乳房自動超音波中的腫瘤。 | zh_TW |
| dc.description.abstract | Automated whole breast ultrasound (ABUS) is becoming a popular screening modality for whole breast examination. Compared to conventional hand-held ultrasound, ABUS could achieve operator-independent and is feasible for mass screening. Because reviewing hundreds of slices in an ABUS image volume is time-consuming, a computer-aided detection (CADe) system based on watershed transform was proposed to accelerate the reviewing. The watershed transform was applied to gather similar tissues around local minima to be homogeneous regions. The likelihoods of being a tumor of the regions were estimated using the quantitative morphology, intensity, and texture features in 2-D/3-D false positive reduction (FPR). The collected database comprised 68 benign and 65 malignant tumors. As a result, the proposed system achieved sensitivities of 100% (133/133), 90% (121/133), and 80% (107/133) with FPs/pass of 9.44, 5.42, and 3.33, respectively. The figure of merit (FOM) of the combination of three feature sets is 0.46 which is significantly better than other feature sets (p-value<0.05). Summarily, the proposed CADe system based on the multidimensional feature sets extracted from the watershed segmentation is promising in detecting tumors in ABUS images. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T10:52:06Z (GMT). No. of bitstreams: 1 ntu-102-R00922104-1.pdf: 5021400 bytes, checksum: 44400396eac52a672586dfc70649cc69 (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 Table of Contents v List of Figures vi List of Tables ix Chapter 1 Introduction 1 Chapter 2 Materials 4 Chapter 3 ABUS Tumor Detection Based on Topographic Watershed 8 3.1 Resolution Reduction and Image Quality Enhancement 9 3.2 Watershed Transform for Tissue Segmentation 12 3.3 Suspicious Abnormality Extraction 15 3.4 2-D/3-D False Positive Reduction 18 3.4.1 Morphology Features 20 3.4.2 Intensity Features 22 3.4.3 Texture Features 23 3.4.4 Statistical Analysis 28 Chapter 4 Experimental Results and Discussion 30 4.1 Experimental Results 30 4.2 Discussion 50 Chapter 5 Conclusion and Future Works 53 Reference 55 | |
| dc.language.iso | en | |
| dc.subject | 乳癌 | zh_TW |
| dc.subject | 電腦輔助乳房腫瘤偵測 | zh_TW |
| dc.subject | 全乳房自動超音波 | zh_TW |
| dc.subject | 分水嶺切割 | zh_TW |
| dc.subject | 多維度的偽陽性篩除 | zh_TW |
| dc.subject | Breast cancer | en |
| dc.subject | Automated whole breast ultrasound | en |
| dc.subject | Watershed segmentation | en |
| dc.subject | Multi-dimensional false positive reduction | en |
| dc.subject | Computer-aided detection | en |
| dc.title | 使用拓樸分水嶺切割於全乳房自動超音波的多維度腫瘤偵測 | zh_TW |
| dc.title | Multi-dimensional Tumor Detection in Automated Whole Breast Ultrasound using Topographic Watershed | 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 | 乳癌,電腦輔助乳房腫瘤偵測,全乳房自動超音波,分水嶺切割,多維度的偽陽性篩除, | zh_TW |
| dc.subject.keyword | Breast cancer,Computer-aided detection,Automated whole breast ultrasound,Watershed segmentation,Multi-dimensional false positive reduction, | en |
| dc.relation.page | 59 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2013-08-09 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-102-1.pdf 未授權公開取用 | 4.9 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。