請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/52592
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張瑞峰 | |
dc.contributor.author | Yu-Ling Hou | en |
dc.contributor.author | 侯玉翎 | zh_TW |
dc.date.accessioned | 2021-06-15T16:19:42Z | - |
dc.date.available | 2020-08-27 | |
dc.date.copyright | 2015-08-27 | |
dc.date.issued | 2015 | |
dc.date.submitted | 2015-08-17 | |
dc.identifier.citation | [1] J. G. Elmore, K. Armstrong, C. D. Lehman, and S. W. Fletcher, 'Screening for breast cancer,' JAMA, vol. 293, pp. 1245-56, Mar 9 2005.
[2] J. Ferlay, H. R. Shin, F. Bray, D. Forman, C. Mathers, and D. M. Parkin, 'Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008,' Int J Cancer, vol. 127, pp. 2893-917, Dec 15 2010. [3] A. Howell, 'The emerging breast cancer epidemic: early diagnosis and treatment,' Breast Cancer Research, vol. 12, 2010. [4] P. S. Y. Cheung, S. W. W. Chan, S. Chan, S. S. Lau, T. T. Wong, M. Ma, A. Wong, and Y. C. Law, 'Benefit of Ultrasonography in the Detection of Clinically and Mammographically Occult Breast Cancer,' World Journal of Surgery, vol. 32, pp. 2593-2598, Dec 2008. [5] M. Funke and C. Villena, 'Breast cancer imaging,' Radiologe, vol. 48, pp. 601-613, Jun 2008. [6] H. D. Nelson, K. Tyne, A. Naik, C. Bougatsos, B. K. Chan, and L. Humphrey, 'Screening for breast cancer: an update for the U.S. Preventive Services Task Force,' Ann Intern Med, vol. 151, pp. 727-37, W237-42, Nov 17 2009. [7] K. Kalmantis, C. Dimitrakakis, C. Koumpis, A. Tsigginou, N. Papantoniou, S. Mesogitis, and A. Antsaklis, 'The contribution of three-dimensional power Doppler imaging in the preoperative assessment of breast tumors: a preliminary report,' Obstet Gynecol Int, vol. 2009, p. 530579, 2009. [8] T. M. Kolb, J. Lichy, and J. H. Newhouse, 'Occult cancer in women with dense breasts: Detection with screening US - Diagnostic yield and tumor characteristics,' Radiology, vol. 207, pp. 191-199, Apr 1998. [9] 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, Jul 2003. [10] V. Corsetti, A. Ferrari, M. Ghirardi, R. Bergonzini, S. Bellarosa, O. Angelini, C. Bani, and S. Ciatto, 'Role of ultrasonography in detecting mammographically occult breast carcinoma in women with dense breasts,' Radiologia Medica, vol. 111, pp. 440-448, Apr 2006. [11] 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,' International Congress Series, vol. 1281, pp. 1075-1080, 2005. [12] 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. [13] Y. Ikedo, D. Fukuoka, T. Hara, H. Fujita, E. Takada, T. Endo, and T. Morita, 'Development of a fully automatic scheme for detection of masses in whole breast ultrasound images,' Medical Physics, vol. 34, pp. 4378-4388, Nov 2007. [14] M. Tozaki, S. Isobe, M. Yamaguchi, Y. Ogawa, M. Kohara, C. Joo, and E. Fukuma, 'Optimal scanning technique to cover the whole breast using an automated breast volume scanner,' Japanese Journal of Radiology, vol. 28, pp. 325-328, May 2010. [15] 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, Mar 2010. [16] 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. [17] J. M. Chang, W. K. Moon, N. Cho, J. S. Park, and S. J. Kim, 'Breast cancers initially detected by hand-held ultrasound: detection performance of radiologists using automated breast ultrasound data,' Acta Radiol, vol. 52, pp. 8-14, Feb 1 2011. [18] F. S. Vernacchia and Z. G. Pena, 'Digital Mammography: Its Impact on Recall Rates and Cancer Detection Rates in a Small Community-Based Radiology Practice,' American Journal of Roentgenology, vol. 193, pp. 582-585, Aug 2009. [19] W. K. Moon, Y. C. Chang, Y. C., M. C. Yang, C. S. Huang, S. C. Chang, G. Y. Huang, and R. F. Chang, 'Automatic Selection of Representative Slice from Cine-Loops of Real-Time Sonoelastography for Classifying Solid Breast Masses,' Ultrasound in Medicine and Biology, vol. 37, pp. 709-718, May 2011. [20] E. K. Kim, J. H. Youk, M. J. Kim, J. Y. Kwak, and E. J. Son, 'Performance of hand-held whole-breast ultrasound based on BI-RADS in women with mammographically negative dense breast,' European Radiology, vol. 21, pp. 667-675, Apr 2011. [21] A. R. Jackson, J. R. Cleverley, and A. C. Bateman, 'Pre-operative localization of breast microcalcification using high-frequency ultrasound,' Clinical Radiology, vol. 52, pp. 924-926, Dec 1997. [22] H. Gufler, C. H. Buitrago-Tellez, H. Madjar, K. H. Allmann, M. Uhl, and A. Rohr-Reyes, 'Ultrasound demonstration of mammographically detected microcalcifications,' Acta Radiologica, vol. 41, pp. 217-221, May 2000. [23] Y. Ikedo, D. Fukuoka, T. Hara, H. Fujita, E. Takada, T. Endo, and T. Morita, 'Development of a fully automatic scheme for detection of masses in whole breast ultrasound images,' Med Phys, vol. 34, pp. 4378-88, Nov 2007. [24] 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,' Eur Radiol, vol. 20, pp. 734-742, Mar 2010. [25] V. G. Maturo, N. R. Zusmer, A. J. Gilson, W. M. Smoak, W. R. Janowitz, B. E. Bear, J. Goddard, and D. E. Dick, 'Ultrasound of the whole breast utilizing a dedicated automated breast scanner,' Radiology, vol. 137, pp. 457-63, Nov 1980. [26] A. P. Harper, E. Kelly-Fry, J. S. Noe, J. R. Bies, and V. P. Jackson, 'Ultrasound in the evaluation of solid breast masses,' Radiology, vol. 146, pp. 731-6, Mar 1983. [27] R. L. Egan and K. L. Egan, 'Detection of breast carcinoma: comparison of automated water-path whole-breast sonography, mammography, and physical examination,' AJR Am J Roentgenol, vol. 143, pp. 493-7, Sep 1984. [28] D. B. Kopans, J. E. Meyer, and K. K. Lindfors, 'Whole-breast US imaging: four-year follow-up,' Radiology, vol. 157, pp. 505-7, Nov 1985. [29] V. P. Jackson, E. Kellyfry, P. A. Rothschild, R. W. Holden, and S. A. Clark, 'Automated Breast Sonography Using a 7.5-Mhz Pvdf Transducer - Preliminary Clinical-Evaluation - Work in Progress,' Radiology, vol. 159, pp. 679-684, Jun 1986. [30] L. W. Bassett, C. Kimme-Smith, L. K. Sutherland, R. H. Gold, D. Sarti, and W. King, 3rd, 'Automated and hand-held breast US: effect on patient management,' Radiology, vol. 165, pp. 103-8, Oct 1987. [31] J. H. Chen, C. S. Huang, K. C. Chien, E. Takada, W. K. Moon, J. H. Wu, N. Cho, Y. F. Wang, and R. F. Chang, 'Breast density analysis for whole breast ultrasound images,' Med Phys, vol. 36, pp. 4933-43, Nov 2009. [32] A. T. Stavros, D. Thickman, C. L. Rapp, M. A. Dennis, S. H. Parker, and G. A. Sisney, 'Solid breast nodules: use of sonography to distinguish between benign and malignant lesions,' Radiology, vol. 196, pp. 123-34, Jul 1995. [33] E. B. Mendelson, W. A. Berg, and C. R. B. Merritt, 'Toward a standardized breast ultrasound lexicon, BI-RADS: Ultrasound,' Seminars in Roentgenology, vol. 36, pp. 217-225, Jul 2001. [34] K. Taylor, P. Britton, S. O'Keeffe, and M. G. Wallis, 'Quantification of the UK 5-point breast imaging classification and mapping to BI-RADS to facilitate comparison with international literature,' British Journal of Radiology, vol. 84, pp. 1005-1010, Nov 2011. [35] L. W. Bassett, 'Mammographic analysis of calcifications,' Radiol Clin North Am, vol. 30, pp. 93-105, Jan 1992. [36] B. Delafontan, J. P. Daures, B. Salicru, F. Eynius, J. Mihura, P. Rouanet, J. L. Lamarque, A. Naja, and H. Pujol, 'Isolated Clustered Microcalcifications - Diagnostic-Value of Mammography - Series of 400 Cases with Surgical Verification,' Radiology, vol. 190, pp. 479-483, Feb 1994. [37] M. Kriege, C. T. Brekelmans, C. Boetes, P. E. Besnard, H. M. Zonderland, I. M. Obdeijn, R. A. Manoliu, T. Kok, H. Peterse, M. M. Tilanus-Linthorst, S. H. Muller, S. Meijer, J. C. Oosterwijk, L. V. Beex, R. A. Tollenaar, H. J. de Koning, E. J. Rutgers, and J. G. Klijn, 'Efficacy of MRI and mammography for breast-cancer screening in women with a familial or genetic predisposition,' N Engl J Med, vol. 351, pp. 427-37, Jul 29 2004. [38] N. F. Boyd, H. Guo, L. J. Martin, L. M. Sun, J. Stone, E. Fishell, R. A. Jong, G. Hislop, A. Chiarelli, S. Minkin, and M. J. Yaffe, 'Mammographic density and the risk and detection of breast cancer,' New England Journal of Medicine, vol. 356, pp. 227-236, Jan 18 2007. [39] C. K. Bent, L. W. Bassett, C. J. D'Orsi, and J. W. Sayre, 'The Positive Predictive Value of BI-RADS Microcalcification Descriptors and Final Assessment Categories,' American Journal of Roentgenology, vol. 194, pp. 1378-1383, May 2010. [40] W. K. Moon, J. G. Im, Y. H. Koh, D. Y. Noh, and A. Park, 'US of mammographically detected clustered microcalcifications,' Radiology, vol. 217, pp. 849-854, Dec 2000. [41] W. K. Moon, J. S. Myung, Y. J. Lee, I. A. Park, D. Y. Noh, and J. G. Im, 'US of ductal carcinoma in situ,' Radiographics, vol. 22, pp. 269-280, Mar-Apr 2002. [42] W. K. Moon, N. Cho, J. H. Cha, S. M. Kim, M. J. Jang, J. M. Chang, and S. Y. Chung, 'Ultrasound-Guided Vacuum-Assisted Biopsy of Microcalcifications Detected at Screening Mammography,' Acta Radiologica, vol. 50, pp. 602-609, 2009. [43] T. Nagashima, H. Hashimoto, K. Oshida, S. Nakano, N. Tanabe, T. Nikaido, K. Koda, and M. Miyazaki, 'Ultrasound Demonstration of Mammographically Detected Microcalcifications in Patients with Ductal Carcinoma in situ of the Breast,' Breast Cancer, vol. 12, pp. 216-20, 2005. [44] F. Stoblen, S. Landt, A. Koninger, J. Hecktor, R. Kimmig, and S. Kummel, 'Detection of microcalcifications by high-resolution B-mode sonography in patients with BI-RADS 4a lesions,' Gynakol Geburtshilfliche Rundsch, vol. 49, pp. 292-8, 2009. [45] F. Stoblen, S. Landt, R. Ishaq, R. Stelkens-Gebhardt, M. Rezai, P. Skaane, J. U. Blohmer, J. Sehouli, and S. Kummel, 'High-frequency Breast Ultrasound for the Detection of Microcalcifications and Associated Masses in BI-RADS 4a Patients,' Anticancer Res, vol. 31, pp. 2575-81, Aug 2011. [46] P. C. Johns and M. J. Yaffe, 'X-ray characterisation of normal and neoplastic breast tissues,' Phys Med Biol, vol. 32, pp. 675-95, Jun 1987. [47] H. Madjar, E. B. Mendelson, and J. Jellins, The practice of breast ultrasound : techniques--findings--differential diagnosis, 2nd ed. Stuttgart ; New York: Thieme, 2008. [48] E. Sedgwick, 'The Breast Ultrasound Lexicon: Breast Imaging Reporting and Data System (BI-RADS),' Seminars in Roentgenology, vol. 46, pp. 245-251, Oct 2011. [49] W. K. Moon, Y. W. Shen, C. S. Huang, S. C. Luo, A. Kuzucan, J. H. Chen, and R. F. Chang, 'Comparative study of density analysis using automated whole breast ultrasound and MRI,' Med Phys, vol. 38, pp. 382-9, Jan 2011. [50] E. S. Ko, B. H. Lee, H. Y. Choi, R. B. Kim, and W. C. Noh, 'Background enhancement in breast MR: Correlation with breast density in mammography and background echotexture in ultrasound,' European Journal of Radiology, vol. 80, pp. 719-723, Dec 2011. [51] Mendelson EB, Baum JK, Berg WA, Merritt CRB, and R. E, Breast Imaging Reporting and Data System, BI-RADS: Ultrasound. Reston, VA: American College of Radiology, 2003. [52] S. K. Yang, W. K. Moon, N. Cho, J. S. Park, J. H. Cha, S. M. Kim, S. J. Kim, and J. G. Im, 'Screening mammography-detected cancers: sensitivity of a computer-aided detection system applied to full-field digital mammograms,' Radiology, vol. 244, pp. 104-11, Jul 2007. [53] M. S. Soo, J. A. Baker, and E. L. Rosen, 'Sonographic detection and sonographically guided biopsy of breast microcalcifications,' AJR Am J Roentgenol, vol. 180, pp. 941-8, Apr 2003. [54] N. Cho, W. K. Moon, J. M. Chang, S. H. Park, C. Y. Lyou, and I. A. Park, 'Ultrasonography-guided vacuum-assisted biopsy of microcalcifications: Comparison of the diagnostic yield of calcified cores and non-calcified cores on specimen radiographs,' Acta Radiologica, vol. 51, pp. 123-7, Mar 2010. [55] M. E. Anderson, M. S. Soo, R. C. Bentley, and G. E. Trahey, 'The detection of breast microcalcifications with medical ultrasound,' J Acoust Soc Am, vol. 101, pp. 29-39, Jan 1997. [56] P. Shankar, 'A statistical model for the ultrasonic backscattered echo from tissue containing microcalcifications,' IEEE Trans Ultrason Ferroelectr Freq Control, vol. 60, pp. 932-42, May 2013. [57] V. Dutt and J. F. Greenleaf, 'Adaptive speckle reduction filter for log-compressed B-scan images,' Ieee Transactions on Medical Imaging, vol. 15, pp. 802-813, Dec 1996. [58] D. S. Bright and E. B. Steel, 'Two-Dimensional Top Hat Filter for Extracting Spots and Spheres from Digital Images,' Journal of Microscopy-Oxford, vol. 146, pp. 191-200, May 1987. [59] J. C. Russ, The Image Processing Handbook, Fourth ed. Boca Raton: CRC Press, 1999. [60] C. S. Huang, W. M. Moon, W. K., S. C. Chang, and R. F. Chang, 'Breast Tumor Classification Using Fuzzy Clustering for Breast Elastography,' Ultrasound in Medicine and Biology, vol. 37, pp. 700-708, May 2011. [61] J. M. Thijssen, B. J. Oosterveld, and R. F. Wagner, 'Gray Level Transforms and Lesion Detectability in Echographic Images,' Ultrasonic Imaging, vol. 10, pp. 171-195, Jul 1988. [62] J. S. Lee, 'Digital Image Smoothing and the Sigma Filter,' Computer Vision Graphics and Image Processing, vol. 24, pp. 255-269, 1983. [63] R. C. Gonzalez and R. E. Woods, Digital image processing, 2nd ed. Upper Saddle River,NJ: Pearson Prentice Hall, 2009. [64] J. A. Sethian, Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science. Cambridge, UK; New York: Cambridge University Press, 1999. [65] A. T. Stavros, C. L. Rapp, and S. H. Parker, Breast ultrasound: Lippincott Williams & Wilkins, 2004. [66] R. F. Chang, W. J. Wu, W. K. Moon, and D. R. Chen, 'Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis,' Ultrasound in Medicine and Biology, vol. 29, pp. 679-86, May 2003. [67] D. Chakraborty, 'Statistical power in observer-performance studies: Comparison of the receiver operating characteristic and free-response methods in tasks involving localization,' Academic Radiology, vol. 9, pp. 147-156, Feb 2002. [68] V. A. McCormack and I. dos Santos Silva, 'Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis,' Cancer Epidemiol Biomarkers Prev, vol. 15, pp. 1159-69, Jun 2006. [69] N. F. Boyd, H. Guo, L. J. Martin, L. Sun, J. Stone, E. Fishell, R. A. Jong, G. Hislop, A. Chiarelli, S. Minkin, and M. J. Yaffe, 'Mammographic density and the risk and detection of breast cancer,' N Engl J Med, vol. 356, pp. 227-36, Jan 18 2007. [70] C. Byrne, C. Schairer, J. Wolfe, N. Parekh, M. Salane, L. A. Brinton, R. Hoover, and R. Haile, 'Mammographic features and breast cancer risk: effects with time, age, and menopause status,' J Natl Cancer Inst, vol. 87, pp. 1622-9, Nov 1 1995. [71] A. Manduca, M. J. Carston, J. J. Heine, C. G. Scott, V. S. Pankratz, K. R. Brandt, T. A. Sellers, C. M. Vachon, and J. R. Cerhan, 'Texture features from mammographic images and risk of breast cancer,' Cancer Epidemiol Biomarkers Prev, vol. 18, pp. 837-45, Mar 2009. [72] D. Kontos, L. C. Ikejimba, P. R. Bakic, A. B. Troxel, E. F. Conant, and A. D. A. Maidment, 'Analysis of Parenchymal Texture with Digital Breast Tomosynthesis: Comparison with Digital Mammography and Implications for Cancer Risk Assessment,' Radiology, vol. 261, pp. 80-91, Oct 2011. [73] D. Kontos, P. R. Bakic, A. K. Carton, A. B. Troxel, E. F. Conant, and A. D. Maidment, 'Parenchymal texture analysis in digital breast tomosynthesis for breast cancer risk estimation: a preliminary study,' Acad Radiol, vol. 16, pp. 283-98, Mar 2009. [74] M. T. Mandelson, N. Oestreicher, P. L. Porter, D. White, C. A. Finder, S. H. Taplin, and E. White, 'Breast density as a predictor of mammographic detection: Comparison of interval- and screen-detected cancers,' J Natl Cancer Inst, vol. 92, pp. 1081-1087, Jul 5 2000. [75] D. S. M. Buist, P. L. Porter, C. Lehman, S. H. Taplin, and E. White, 'Factors contributing to mammography failure in women aged 40-49 years,' J Natl Cancer Inst, vol. 96, pp. 1432-1440, Oct 6 2004. [76] W. A. Berg, Z. Zhang, D. Lehrer, R. A. Jong, E. D. Pisano, R. G. Barr, M. Bohm-Velez, M. C. Mahoney, W. P. Evans, L. H. Larsen, M. J. Morton, E. B. Mendelson, D. M. Farria, J. B. Cormack, H. S. Marques, A. Adams, N. M. Yeh, G. Gabrielli, and A. Investigators, '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, vol. 307, pp. 1394-1404, Apr 4 2012. [77] W. K. Moon, C. M. Lo, J. M. Chang, M. S. Bae, W. H. Kim, C. S. Huang, J. H. Chen, M. H. Kuo, and R. F. Chang, 'Rapid breast density analysis of partial volumes of automated breast ultrasound images,' Ultrason Imaging, vol. 35, pp. 333-43, Oct 2013. [78] Y. Ikedo, T. Morita, D. Fukuoka, T. Hara, G. Lee, H. Fujita, E. Takada, and T. Endo, 'Automated analysis of breast parenchymal patterns in whole breast ultrasound images: preliminary experience,' International Journal of Computer Assisted Radiology and Surgery, vol. 4, pp. 299-306, May 2009. [79] American College of Radiology, Breast Imaging Reporting and Data System (BI-RADS) : Ultrasound. Reston, (VA) American College of Radiology, 2003. [80] Y. H. Guo and H. D. Cheng, 'New neutrosophic approach to image segmentation,' Pattern Recognition, vol. 42, pp. 587-595, May 2009a. [81] F. Smarandache, A Unifying Field in Logics Neutrosophic Logic. Neutrosophy, Neutrosophic Set,NeutrosophicProbability, 4nd ed. American Research Press, 2003. [82] M. Zhang, L. Zhang, and H. D. Cheng, 'A neutrosophic approach to image segmentation based on watershed method,' Signal Processing, vol. 90, pp. 1510-1517, May 2010. [83] P. Kraipeerapun and C. C. Fung, 'Binary classification using ensemble neural networks and interval neutrosophic sets,' Neurocomputing, vol. 72, pp. 2845-2856, Aug 2009. [84] A. Sengur and Y. H. Guo, 'Color texture image segmentation based on neutrosophic set and wavelet transformation,' Computer Vision and Image Understanding, vol. 115, pp. 1134-1144, Aug 2011. [85] Y. H. Guo, H. D. Cheng, J. W. Tian, and Y. T. Zhang, 'A Novel Approach to Speckle Reduction in Ultrasound Imaging,' Ultrasound in Medicine and Biology, vol. 35, pp. 628-640, Apr 2009b. [86] J. Shan, H. D. Cheng, and Y. Wang, 'A novel segmentation method for breast ultrasound images based on neutrosophic l-means clustering,' Med Phys, vol. 39, pp. 5669-82, Sep 2012. [87] H. D. Cheng, Y. Guo, and Y. Zhang, 'A Novel Image Segmentation Approach Based on Neutrosophic Set and Improved Fuzzy C-Means Algorithm,' New Mathematics and Natural Computation, vol. 7, pp. 155-171, 2011. [88] L. Watson, 'The role of ultrasound in breast imaging,' Radiol Technol, vol. 71, pp. 441-59; quiz 460-2, May-Jun 2000. [89] Y. H. Guo, H. D. Cheng, W. Zhao, and Y. Zhang, 'A Novel Image Segmentation Algorithm Based on Fuzzy C-Means Algorithm and Neutrosophic Set,' Proceedings of the 11th Joint Conference on Information Sciences, 2008. [90] R. A. Groeneveld and G. Meeden, 'Measuring Skewness and Kurtosis,' Statistician, vol. 33, pp. 391-399, 1984. [91] D. Doric, E. Nikolic-Doric, V. Jevremovic, and J. Malisic, 'On measuring skewness and kurtosis,' Quality & Quantity, vol. 43, pp. 481-493, May 2009. [92] H. O. Lancaster, An introduction to medical statistics. New York,: Wiley, 1973. [93] C. M. Bishop, Pattern recognition and machine learning. New York: Springer, 2006. [94] B. Everitt, The Cambridge dictionary of statistics, 3rd ed. Cambridge, UK ; New York: Cambridge University Press, 2006. [95] D. N. Joanes and C. A. Gill, 'Comparing measures of sample skewness and kurtosis,' Journal of the Royal Statistical Society Series D-the Statistician, vol. 47, pp. 183-189, 1998. [96] V. Cherkassky, 'The nature of statistical learning theory~,' IEEE Trans Neural Netw, vol. 8, p. 1564, 1997. [97] M. Pontil and A. Verri, 'Support Vector Machines for 3D object recognition,' Ieee Transactions on Pattern Analysis and Machine Intelligence, vol. 20, pp. 637-646, Jun 1998. [98] O. Chapelle, P. Haffner, and V. N. Vapnik, 'Support vector machines for histogram-based image classification,' IEEE Trans Neural Netw, vol. 10, pp. 1055-64, 1999. [99] J. Behnke, 'Discovering statistics using SPSS.,' Politische Vierteljahresschrift, vol. 47, pp. 751-753, Dec 2006. [100] J. R. Landis and G. G. Koch, 'The measurement of observer agreement for categorical data,' Biometrics, vol. 33, pp. 159-74, Mar 1977. [101] American College of Radiology, Breast Imaging Reporting and Data System (BI-RADS) : Mammography. Reston, (VA) American College of Radiology, 2003. [102] W. H. Kim, W. K. Moon, S. J. Kim, A. Yi, B. L. Yun, N. Cho, J. M. Chang, H. R. Koo, M. Y. Kim, M. S. Bae, S. H. Lee, J. Y. Kim, and E. H. Lee, 'Ultrasonographic assessment of breast density,' Breast Cancer Research and Treatment, vol. 138, pp. 851-859, Apr 2013. [103] R. Blend, D. F. Rideout, L. Kaizer, P. Shannon, B. Tudorroberts, and N. F. Boyd, 'Parenchymal Patterns of the Breast Defined by Real-Time Ultrasound,' European Journal of Cancer Prevention, vol. 4, pp. 293-298, Aug 1995. [104] L. Kaizer, E. K. Fishell, J. W. Hunt, F. S. Foster, and N. F. Boyd, 'Ultrasonographically Defined Parenchymal Patterns of the Breast - Relationship to Mammographic Patterns and Other Risk-Factors for Breast-Cancer,' British Journal of Radiology, vol. 61, pp. 118-124, Feb 1988. [105] W. A. Berg, J. D. Blume, J. B. Cormack, and E. B. Mendelson, 'Operator dependence of physician-performed whole-breast US: Lesion detection and characterization,' Radiology, vol. 241, pp. 355-365, Nov 2006. [106] C. Klifa, J. Carballido-Gamio, L. Wilmes, A. Laprie, J. Shepherd, J. Gibbs, B. Fan, S. Noworolski, and N. Hylton, 'Magnetic resonance imaging for secondary assessment of breast density in a high-risk cohort,' Magn Reson Imaging, vol. 28, pp. 8-15, Jan 2010. [107] K. Nie, J. H. Chen, S. Chan, M. K. Chau, H. J. Yu, S. Bahri, T. Tseng, O. Nalcioglu, and M. Y. Su, 'Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI,' Med Phys, vol. 35, pp. 5253-62, Dec 2008. [108] V. King, J. D. Brooks, J. L. Bernstein, A. S. Reiner, M. C. Pike, and E. A. Morris, 'Background Parenchymal Enhancement at Breast MR Imaging and Breast Cancer Risk,' Radiology, vol. 260, pp. 50-60, Jul 2011. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/52592 | - |
dc.description.abstract | 根據統計,乳癌是全球女性因癌症死亡的第二大主因,而早期發現並治療是目前降低致死率最有效的方式。為了能在早期階段篩檢出乳癌,許多關於乳癌預測指標的相關研究被提出。而在所有非侵入式的篩檢方式當中,超音波是一種簡單又有效的技術。近年來,超音波影像不論是在臨床或是研究應用中,都扮演了深具應用價值的角色。儘管目前傳統的二維(2-D)超音波技術已被廣泛運用,但單靠2D 影像卻難以傳達乳房的整體特徵,因此,三維(3-D)超音波的影像裝置被提出。三維(3-D)超音波可以全方位完整呈現乳房的結構,有能力提供一個更廣闊的方向來描述病變特徵,不論是應用在乳癌早期預測指標或是偵測或診斷的研究上,都是很好的影像系統,可提供更多細節資訊及幫助醫生減少誤判的機率。本篇研究主要的目的是利用超音波影像量化分析各種乳癌預測指標,包含微鈣化(microcalcification)及回音組織樣本 (background echotexture pattern),使超音波影像在常規的檢查中,能篩檢出早期乳房病變,以便早期治療,降低致死率。 | zh_TW |
dc.description.abstract | Breast cancer is the global second-leading cause of cancer death among women.
If any possible cancer symptom could be detected in early stages, we can prevent it from getting into an advanced state and reduced lethality. Many studies of predictive markers were proposed in order to inspect breast cancer in early stage. Ultrasound (US) is a simple and effective modality in all non-invasive inspect and plays an important role whether in clinical or research. Although the conventional two-dimensional (2-D) US techniques of the breast at present have been widely used, 2-D images are not enough to transmit the entire characteristics of breast. Therefore, the three-dimensional (3-D) breast US is proposed to improve drawbacks of 2-D breast US. The three-dimensional (3-D) US is also called automated breast ultrasound (ABUS). ABUS can fully provide the architecture of breast in all aspects and therefore is capable of offering a more comprehensive way to characterize pathological features of breast cancer. ABUS is a useful image system, whether it uses in detection, diagnosis or predictive markers in early stage. It provides more detail image information of breast cancer and assists physicians to reduce misdiagnosis. In this study, we present quantitative analysis of predictive markers which include microcalcification and background echotexture for breast cancer using US images. We expected the quantitative analysis of predictive markers for breast cancer could detect the non-palpate breast lesion in early stage in routine examination. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T16:19:42Z (GMT). No. of bitstreams: 1 ntu-104-D98945015-1.pdf: 7713847 bytes, checksum: 2897555deed392cbd8ddeb16a97e0671 (MD5) Previous issue date: 2015 | en |
dc.description.tableofcontents | 口試委員審定書 ............................................................................................ I
誌 謝 .......................................................................................................... II 中文摘要 .................................................................................................... III ABSTRACT ................................................................................................. IV CONTENTS ................................................................................................ VI LIST OF FIGURES ........................................................................................ VIII LIST OF TABLES .......................................................................................... XIII CHAPTER 1 INTRODUCTION .................................................................... 1 1.1. RESEARCH MOTIVATION ...................................................................... 1 1.2. ISSUE DESCRIPTIONS ........................................................................... 3 1.3. ORGANIZATION ................................................................................... 5 CHAPTER 2 REVIEW OF RELATED WORKS .................................................. 7 2.1. REVIEW OF ULTRASOUND IMAGING ...................................................... 7 2.1.1 B-mode Imaging ............................................................................... 7 2.1.2 Automated breast ultrasound (ABUS) ................................................. 10 2.2. BREAST IMAGING REPORTING AND DATA SYSTEM (BI-RADS) ................. 12 2.3. REVIEW OF PREVIOUS STUDIES FOR MICROCALCIFICATION ................... 19 2.4. REVIEW OF PREVIOUS STUDIES FOR BACKGROUND ECHOTEXTURE ........ 21 CHAPTER 3 AUTOMATIC DETECTION OF MICROCALCIFICATIONS IN BREAST ULTRASOUND ............................................................................................. 23 3.1. INTRODUCTION ................................................................................... 23 3.2. MATERIALS .......................................................................................... 25 3.3. METHOD ............................................................................................. 27 3.3.1 Lesion Segmentation ......................................................................... 28 3.3.2 Adaptive Speckle Reduction, Top Hat Filter, and Labeling .................. 32 3.3.3 Selection of Microcalcifications Using Three Criteria .......................... 35 A. Mean criterion ....................................................................................... 35 B. Single point criterion ............................................................................. 36 C. Brightness criterion ............................................................................... 37 3.3.4 Statistical analysis ............................................................................ 38 3.4. EXPERIMENTAL RESULT ....................................................................... 39 3.5. DISCUSSION ....................................................................................... 45 3.6. CONCLUSION ..................................................................................... 48 CHAPTER 4 QUANTITATIVE ANALYSIS OF BREAST ECHOTEXTURE PATTERNS IN AUTOMATED BREAST ULTRASOUND IMAGES .............................................. 49 4.1. INTRODUCTION ................................................................................. 49 4.2. MATERIALS ........................................................................................ 51 4.3. METHOD ........................................................................................... 52 4.3.1 Fibroglandular tissue segmentation ................................................. 54 A. Neutrosophic image transformation ..................................................... 56 B. Entropy ................................................................................................ 57 C. α-mean and β-enhancement operation .............................................. 58 D. Fuzzy c-mean clustering .................................................................... 60 E. The upper boundary definition ........................................................... 63 F. The lower boundary definition ............................................................ 66 4.3.2 Features extraction ......................................................................... 69 4.3.3 Classification and Evaluation of the Performance .............................. 71 4.4. EXPERIMENTAL RESULT ..................................................................... 73 4.5. DISCUSSION ....................................................................................... 82 4.6. CONCLUSION ..................................................................................... 86 CHAPTER 5 CONCLUSIONS AND FUTURE DIRECTIONS ...................... 88 5.1. CONCLUSIONS ................................................................................. 88 5.2. FUTURE WORKS ............................................................................... 89 REFERENCES ............................................................................................ 90 | |
dc.language.iso | en | |
dc.title | 使用乳房超音波影像量化分析乳癌預測指標 | zh_TW |
dc.title | Quantitative Analysis of Predictive Markers for Breast Cancer Using Ultrasound Images | en |
dc.type | Thesis | |
dc.date.schoolyear | 103-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 黃俊升,張允中,羅崇銘,陳啟禎 | |
dc.subject.keyword | 乳癌,預測指標,微鈣化,回音組織樣本, | zh_TW |
dc.subject.keyword | breast cancer,predictive markers,microcalcification,background echotexture pattern, | en |
dc.relation.page | 100 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2015-08-17 | |
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 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-104-1.pdf 目前未授權公開取用 | 7.53 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。