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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張瑞峰 | |
dc.contributor.author | Yu-Hsuan Chu | en |
dc.contributor.author | 朱于萱 | zh_TW |
dc.date.accessioned | 2021-06-15T13:53:02Z | - |
dc.date.available | 2015-12-01 | |
dc.date.copyright | 2015-12-01 | |
dc.date.issued | 2015 | |
dc.date.submitted | 2015-09-12 | |
dc.identifier.citation | [1] Torre, L.A., F. Bray, R.L. Siegel, J. Ferlay, J. Lortet-Tieulent, et al., Global Cancer Statistics,. CA: a cancer journal for clinicians, 2015. 65: p. 87-108.
[2] Mathers, C.D., D.M. Fat, and J. Boerma, The global burden of disease: 2004 update2008: World Health Organization. [3] Otto, S.J., J. Fracheboud, A.L. Verbeek, R. Boer, J.C.I.Y. Reijerink-Verheij, et al., Mammography Screening and Breast Cancer Mortality-Response. Cancer Epidemiology Biomarkers & Prevention, 2012. 21(5): p. 870-871. [4] Nothacker, M., V. Duda, M. Hahn, M. Warm, F. Degenhardt, et al., Early detection of breast cancer: benefits and risks of supplemental breast ultrasound in asymptomatic women with mammographically dense breast tissue. A systematic review. Bmc Cancer, 2009. 9(1): p. 335. [5] Kolb, T.M., J. Lichy, and J.H. Newhouse, Occult cancer in women with dense breasts: detection with screening US--diagnostic yield and tumor characteristics. Radiology, 1998. 207(1): p. 191-199. [6] Berg, W.A., Z. Zhang, D. Lehrer, R.A. Jong, E.D. Pisano, et al., Detection of Breast Cancer With Addition of Annual Screening Ultrasound or a Single Screening MRI to Mammography in Women With Elevated Breast Cancer Risk. Jama-Journal of the American Medical Association, 2012. 307(13): p. 1394-1404. [7] Kelly, K.M., 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, 2010. 20(3): p. 734-742. [8] Chan, S.W., P.S. Cheung, S. Chan, S.S. Lau, T.T. Wong, et al., Benefit of ultrasonography in the detection of clinically and mammographically occult breast cancer. World journal of surgery, 2008. 32(12): p. 2593-2598. [9] Timmers, J.M.H., H.J. van Doorne-Nagtegaal, H.M. Zonderland, H. van Tinteren, O. Visser, et al., The Breast Imaging Reporting and Data System (BI-RADS) in the Dutch breast cancer screening programme: its role as an assessment and stratification tool. European Radiology, 2012. 22(8): p. 1717-1723. [10] Barr, R.G., Z. Zhang, J.B. Cormack, E.B. Mendelson, and W.A. Berg, Probably Benign Lesions at Screening Breast US in a Population with Elevated Risk: Prevalence and Rate of Malignancy in the ACRIN 6666 Trial. Radiology, 2013. 269(3): p. 701-712. [11] Berg, W.A., J.D. Blume, J.B. Cormack, and E.B. Mendelson, Training the ACRIN 6666 Investigators and Effects of Feedback on Breast Ultrasound Interpretive Performance and Agreement in BI-RADS Ultrasound Feature Analysis. American Journal of Roentgenology, 2012. 199(1): p. 224-235. [12] Moon, W.K., C.M. Lo, N. Cho, J.M. Chang, C.S. Huang, et al., Computer-aided diagnosis of breast masses using quantified BI-RADS findings. Computer Methods and Programs in Biomedicine, 2013. 111(1): p. 84-92. [13] Smeraldi, F., A Nonparametric approach to face detection using ranklets. Audio-and Video-Based Biometric Person Authentication, Proceedings, 2003. 2688: p. 351-359. [14] Berg, W.A., D.O. Cosgrove, C.J. Dore, F.K.W. Schafer, W.E. Svensson, et al., Shear-wave Elastography Improves the Specificity of Breast US: The BE1 Multinational Study of 939 Masses. Radiology, 2012. 262(2): p. 435-449. [15] Fleury, E., J. Fleury, S. Piato, and D. Roveda Jr, New elastographic classification of breast lesions during and after compression. Diagn Interv Radiol, 2009. 15(2): p. 96-103. [16] Konno, S., E. Takada, N. Ejiri, M. Kawamata, N. Takase, et al., Stereoscopic images of breast tumors using 3D real-time tissue elastography. Journal of Medical Ultrasonics, 2015. 42(3): p. 365-371. [17] Barr, R.G., S. Destounis, L.B. Lackey, W.E. Svensson, C. Balleyguier, et al., Evaluation of Breast Lesions Using Sonographic Elasticity Imaging A Multicenter Trial. Journal of Ultrasound in Medicine, 2012. 31(2): p. 281-287. [18] Leong, L., L. Sim, Y. Lee, F. Ng, C. Wan, et al., A prospective study to compare the diagnostic performance of breast elastography versus conventional breast ultrasound. Clinical radiology, 2010. 65(11): p. 887-894. [19] Sadigh, G., R.C. Carlos, C.H. Neal, and B.A. Dwamena, Accuracy of quantitative ultrasound elastography for differentiation of malignant and benign breast abnormalities: a meta-analysis. Breast cancer research and treatment, 2012. 134(3): p. 923-931. [20] Bartolotta, T., R. Ienzi, A. Cirino, C. Genova, F. Ienzi, et al., Characterisation of indeterminate focal breast lesions on grey-scale ultrasound: role of ultrasound elastography. La radiologia medica, 2011. 116(7): p. 1027-1038. [21] Raza, S., A. Odulate, E.M. Ong, S. Chikarmane, and C.W. Harston, Using real-time tissue elastography for breast lesion evaluation our initial experience. Journal of Ultrasound in Medicine, 2010. 29(4): p. 551-563. [22] Thomas, A., F. Degenhardt, A. Farrokh, S. Wojcinski, T. Slowinski, et al., Significant differentiation of focal breast lesions: calculation of strain ratio in breast sonoelastography. Academic radiology, 2010. 17(5): p. 558-563. [23] Jung, H.J., S.Y. Hahn, H.Y. Choi, S.H. Park, and H.K. Park, Breast Sonographic Elastography Using an Advanced Breast Tissue-Specific Imaging Preset Initial Clinical Results. Journal of Ultrasound in Medicine, 2012. 31(2): p. 273-280. [24] Stachs, A., S. Hartmann, J. Stubert, M. Dieterich, A. Martin, et al., Differentiating Between Malignant and Benign Breast Masses: Factors Limiting Sonoelastographic Strain Ratio. Ultraschall in Der Medizin, 2013. 34(2): p. 131-136. [25] Leong, L.C.H., L.S.J. Sim, Y.S. Lee, F.C. Ng, C.M. Wan, et al., A prospective study to compare the diagnostic performance of breast elastography versus conventional breast ultrasound. Clinical Radiology, 2010. 65(11): p. 887-894. [26] Xu, H.Y., M. Rao, T. Varghese, A. Sommer, S. Baker, et al., Axial-Shear Strain Imaging for Differentiating Benign and Malignant Breast Masses. Ultrasound in Medicine and Biology, 2010. 36(11): p. 1813-1824. [27] Thittai, A.K., J.M. Yamal, L.M. Mobbs, C.M. Kraemer-Chant, S. Chekuri, et al., Axial-Shear Strain Elastography for Breast Lesion Classification: Further Results from in Vivo Data. Ultrasound in Medicine and Biology, 2011. 37(2): p. 189-197. [28] Lo, C.M., Y.P. Chen, Y.C. Chang, C. Lo, C.S. Huang, et al., Computer-Aided Strain Evaluation for Acoustic Radiation Force Impulse Imaging of Breast Masses. Ultrasonic Imaging, 2014. 36(3): p. 151-166. [29] Lo, C.-M., Y.-C. Chang, Y.-W. Yang, C.-S. Huang, and R.-F. Chang, Quantitative breast mass classification based on the integration of B-mode features and strain features in elastography. Computers in Biology and Medicine, 2015. 64: p. 91-100. [30] Moon, W.K., S.-C. Chang, J.M. Chang, N. Cho, C.-S. Huang, et al., Classification of Breast Tumors Using Elastographic and B-mode Features: Comparison of Automatic Selection of Representative Slice and Physician-Selected Slice of Images. Ultrasound in medicine & biology, 2013. [31] Moon, W.K., 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 & biology, 2011. 37(4): p. 539-548. [32] Dong, Q., Largest Connected Component of a k-ary n-cube with Faulty Vertices. Journal of Information Science and Engineering, 2013. 29(4): p. 777-783. [33] Suri, J.S., Advances in diagnostic and therapeutic ultrasound imaging2008: Artech House. [34] Farneback, G. and C.F. Westin, Improving Deriche-style recursive Gaussian filters. Journal of Mathematical Imaging and Vision, 2006. 26(3): p. 293-299. [35] Haralick, R.M., K. Shanmugam, and I.H. Dinstein, Textural features for image classification. Systems, Man and Cybernetics, IEEE Transactions on, 1973(6): p. 610-621. [36] Sudarshan, V.K., E.Y.K. Ng, U.R. Acharya, S.M. Chou, R.S. Tan, et al., Computer-aided diagnosis of Myocardial Infarction using ultrasound images with DWT, GLCM and HOS methods: A comparative study. Computers in Biology and Medicine, 2015. 62: p. 86-93. [37] Yang, M.C., W.K. Moon, Y.C.F. Wang, M.S. Bae, C.S. Huang, et al., Robust Texture Analysis Using Multi-Resolution Gray-Scale Invariant Features for Breast Sonographic Tumor Diagnosis. Ieee Transactions on Medical Imaging, 2013. 32(12): p. 2262-2273. [38] Bribiesca, E., An easy measure of compactness for 2D and 3D shapes. Pattern Recognition, 2008. 41(2): p. 543-554. [39] Meinel, L.A., 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, 2007. 25(1): p. 89-95. [40] Shen, W.-C., R.-F. Chang, W.K. Moon, Y.-H. Chou, and C.-S. Huang, Breast ultrasound computer-aided diagnosis using BI-RADS features. Academic radiology, 2007. 14(8): p. 928-939. [41] Mulchrone, K.F. and K.R. Choudhury, Fitting an ellipse to an arbitrary shape: implications for strain analysis. Journal of structural geology, 2004. 26(1): p. 143-153. [42] Quiming, Z. and P. Lay-Kheng. A transformation-invariant recursive subdivision method for shape analysis. in Pattern Recognition, 1988., 9th International Conference on. 1988. IEEE. [43] Ulanovsky, A. and G. Pröhl, A practical method for assessment of dose conversion coefficients for aquatic biota. Radiation and environmental biophysics, 2006. 45(3): p. 203-214. [44] Itoh, A., E. Ueno, E. Tohno, H. Kamma, H. Takahashi, et al., Breast Disease: Clinical Application of US Elastography for Diagnosis. Radiology, 2006. 239(2): p. 341-350. [45] Bezdek, J.C., Pattern recognition with fuzzy objective function algorithms1981: Kluwer Academic Publishers. [46] Jain, A.K., Fundamentals of digital image processing. Vol. 3. 1989: Prentice-Hall Englewood Cliffs. [47] Sprinthall, R.C. and S.T. Fisk, Basic statistical analysis1990: Prentice Hall Englewood Cliffs, NJ. [48] Hosmer Jr, D.W., S. Lemeshow, and R.X. Sturdivant, Applied logistic regression2013: Wiley. com. [49] Wong, T.T., Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognition, 2015. 48(9): p. 2839-2846. [50] Ma, H., A.I. Bandos, and D. Gur, On the use of partial area under the ROC curve for comparison of two diagnostic tests. Biometrical Journal, 2015. 57(2): p. 304-320. [51] Lai, Y.-C., Y.-S. Huang, D.-W. Wang, C.-M. Tiu, Y.-H. Chou, et al., Computer-Aided Diagnosis for 3-D Power Doppler Breast Ultrasound. Ultrasound in medicine & biology, 2013. [52] Masotti, M. and R. Campanini, Texture classification using invariant ranklet features. Pattern Recognition Letters, 2008. 29(14): p. 1980-1986. [53] Masotti, M., A ranklet-based image representation for mass classification in digital mammograms. Medical Physics, 2006. 33(10): p. 3951-3961. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/51846 | - |
dc.description.abstract | 乳癌為女性癌症中的主要的死因。然而,如果乳癌能早期被偵測到且給予適當的治療,則可降低乳癌的致死率。近年來許多研究顯示利用乳房彈性超音波做診斷,可獲得優於傳統 B-mode 超音波的診斷效果。彈性可表現出腫瘤的軟硬度,而腫瘤軟硬度的差異可用於診斷腫瘤。近來,三維彈性超音波不僅可取得傳統三維 B-mode 影像更可取得不同截切面的彈性影像。因此,此篇研究主要的目的是利用三維乳房彈性超音波進行電腦輔助診斷腫瘤。首先,由 B-mode 影像切割出的腫瘤區域擷取出紋理特徵、形狀特徵、以及建立最接近腫瘤的橢圓模型,取得腫瘤與此模型的異同點特徵,再從彈性影像中對應的腫瘤區域擷取彈性特徵。最後,再利用這些擷取出的特徵來診斷腫瘤的良惡性。在此實驗中採用了 159 個病理驗證過的腫瘤,包含 110 個良性病例以及 49 個惡性病例。經由實驗結果,結合紋理特徵、形狀特徵和彈性特徵會得到最佳的結果,可達到準確率 81% (129/159),靈敏性 78% (38/49),特異性 83% (91/110),以及 ROC 曲線面積 0.8512。因此,使用三維乳房彈性超音波可以有效的診斷腫瘤良惡性。 | zh_TW |
dc.description.abstract | Breast cancer is the leading cause of cancer death for women. The mortality rate of breast cancer can be greatly reduced if a proper treatment is adopted after an early detection. Recently, many studies have shown that adding elastography examination can improve the diagnostic performance comparing to using only conventional ultrasound. Elastography can estimate the tissue stiffness by calculating the tissue displacement under a certain force. The stiffness of benign and malignant tumors can be used to be features for classifying tumors. Therefore, this study proposed a computer-aided detection (CAD) system using 3-D B-mode ultrasound and elastographic breast images. The CAD system proposed in this study using morphology, texture, and elastography features extracted from the segmented B-mode tumor area. Combining these feature sets in a binary regression model generated the malignancy estimation model.The diagnostic performance was validated by using 110 benign and 49 malignant breast lesions. The performance of combinating gray-level co-occurrence matrix (GLCM), ranklet textures,
shape and elastography features achieved an accuracy of 81% (129/159), a sensitivity of 76% (38/49), a specificity of 83% (80/110), and an Az value of 0.8512. Summarily, the combination of B-mode and elastography features is effective to the classification of breast tumor. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T13:53:02Z (GMT). No. of bitstreams: 1 ntu-104-R02945026-1.pdf: 1644764 bytes, checksum: b652edc248483601c0d8948a818073ab (MD5) Previous issue date: 2015 | en |
dc.description.tableofcontents | 口試委員會審定書 .................................. i
致謝 ............................................ ii 摘要 ............................................ iv Abstract ........................................ v Table of Contents .............................. vi List of Figures ............................... vii List of Tables ................................. ix Chapter 1 Introduction .......................... 1 Chapter 2 Materials ............................. 4 2.1 Patients and Lesion Characters .............. 4 2.2 Data Acquisition ............................ 5 Chapter 3 The Tumor Diagnosis Method ............ 6 3.1 Tumor Region Segmentation ................... 8 3.1.1 Sigmoid Filter Operation........................................ 8 3.1.2 Smoothing Recursive Gaussian Filter Operation ................................................. 9 3.1.3 Largest Connected Component Operation .... 11 3.1.4 Contour Smoothing Operation .............. 12 3.2 Feature Extraction ......................... 14 3.2.1 Texture Features ......................... 14 3.2.2 Morphology Features ...................... 18 3.2.3 Elastographic features ................... 23 3.3 Classification ............................. 26 3.3.1 Feature analysis ......................... 26 3.3.2 Tumor classification ..................... 26 Chapter 4 Experiment Results and Discussion .... 28 4.1 Experiment environment ..................... 28 4.2 Statistic analysis results ................. 28 4.3 Result and Discussion ...................... 32 Chapter 5 Conclusion and Future Works .......... 47 References ..................................... 49 | |
dc.language.iso | en | |
dc.title | 3D 乳房彈性超音波之電腦腫瘤診斷 | zh_TW |
dc.title | Computer-aided Tumor Diagnosis for 3-D Breast Elastography | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 羅崇銘,陳啟禎 | |
dc.subject.keyword | 乳癌,三維彈性超音波,電腦輔助診斷, | zh_TW |
dc.subject.keyword | breast cancer,3-D elastography,computer-aided diagnosis, | en |
dc.relation.page | 53 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2015-09-14 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
顯示於系所單位: | 生醫電子與資訊學研究所 |
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