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研究生:吳方荃
研究生(外文):Fang-Chuan Wu
論文名稱:基於模糊理論的全自動乳房超音波系統參數設定
論文名稱(外文):Automatic Breast Ultrasound Parameter Setting Based on Fuzzy Algorithm
指導教授:張瑞峰張瑞峰引用關係
指導教授(外文):Reuy-Cheng Chang
學位類別:碩士
校院名稱:國立中正大學
系所名稱:資訊工程所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:英文
論文頁數:63
中文關鍵詞:模糊理論增益超音波影像焦距參數調整
外文關鍵詞:gain settingultrasound imagefocus zoneparameter settingfuzzy
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超音波乳房篩檢在乳癌診斷上一直扮演很重要的角色。用來做乳房篩檢的超音波系統中有釵h可調整的參數,比方說增益、焦距設定、時間增益補償、雜訊抑制…等參數。對於經驗較少的超音波操作員來說,正確地調整這些參數以取得最佳品質影像是一大挑戰。而影響乳房超音波影像品質最大的參數為增益值及焦距設定。因此,本篇論文中提出一個基於模糊理論的增益及焦距自動調整演算法,用以幫助超音波操作員取得最佳品質的影像,並縮短他們做超音波檢查所花費的時間。我們使用權重亮度平均及HIST參數來評估影像的增益設定是否正確,並對該設定做進一步的調整,以得到最適合的影像亮度。在焦距設定部分,我們使用細棒演算法來降低雜訊影響且強化邊界資訊後;對圖中每個像素做分類,分成腫瘤及一般組織;接著利用連通分量圖演算法將疑似腫瘤的區域連接起來;再利用事先定義的條件來找出腫瘤的區域。套用以上方法來找出腫瘤的位置,並將焦距設定在腫瘤的中間及上下兩端。根據實驗結果,幾乎所有的腫瘤都能被找到,並正確設定焦距位置;由該演算法選出的增益設定跟醫生所選的相差甚少,系統準確高達為九成。實驗結果證明這個方法可適用在乳房超音波系統,且能獲得很好的效果。
In the last decade, ultrasound imaging plays an important role in the field of cancer diagnosis because of its convenience and non-invasive. The adjustable parameters of echo systems include gain, focus, time gain compensation, noise reducing controller, and so on. However, adjusting these parameters is too complex for an inexperienced sonographer to obtain good quality images. In this paper, we propose a new approach to intelligently select the gain and focus parameters of echo systems by using the fuzzy algorithm. The weighted luminance average (WLA) and HIST parameter are calculated for evaluating whether the gain setting is proper. The gain setting is determined by the fuzzy unit that has two HIST parameters for input. After obtaining an ultrasound image with well contrast, stick detection, pixel classification, connected component algorithm, and tumor criteria are used to find out the location of the tumor and then the focus zone could be set on the center of tumor correctly. The purpose of this thesis is to develop an automatic ultrasound parameter selection method to ensure the quality of ultrasound images and assure the images are qualified to ACR guidelines for Breast US. In clinical practice, it can reduce the sonographers’ time for image acquisition and make sure the quality of each image. According to the experimental results, it shows that achieving automatic parameter selection is feasible and it is indeed a useful feature for obtaining good quality images.
摘 要 I
ABSTRACT III
ACKNOWLEDGEMENTS V
TABLE OF CONTENTS VI
LIST OF FIGURES VII
LIST OF TABLES X
CHAPTER 1 INTRODUCTION 1
CHAPTER 2 BACKGROUND 5
2.1. Breast Ultrasound 5
2.2. Fuzzy System 8
2.3. Image Data Acquisition System 12
CHAPTER 3 AUTOMATIC US PARAMETER SELECTION 14
3.1. Stick Detection 15
3.2. Automatic Gain Control 16
3.2.1 Weighted Luminance Average 17
3.2.2 HIST Parameter 18
3.2.3 Fuzzy System 20
3.3. Automatic Focus Control 23
3.3.1 Pixel Classification 23
3.3.2 Connected Component 28
3.3.3 Tumor Criteria 30
CHAPTER 4 EXPERIMENTAL RESULTS 32
4.1. AGC Experimental Results 33
4.2. AFC Experimental Results 39
CHAPTER 5 CONCLUSION AND FURTHER WORKS 47
REFERENCE 49
[1] C.S. Huang, K.J. Chang, and C.Y. Shen, "Breast cancer screening in Taiwan and China," Breast Dis., vol. 13 pp. 41-48, 2001.
[2] B.J. Asua, "Mammography for breast cancer screening," Rev. Esp. Salud Publica, vol. 79, no. 5, pp. 517-520, Sept. 2005.
[3] R. Ballard-Barbash et al., "Breast Cancer Surveillance Consortium: a national mammography screening and outcomes database," AJR Am. J. Roentgenol., vol. 169, no. 4, pp. 1001-1008, Oct. 1997.
[4] P.B. Gordon, "Ultrasound for breast cancer screening and staging," Radiol. Clin. North Am., vol. 40, no. 3, pp. 431-441, May 2002.
[5] W. Teh and A.R. Wilson, "The role of ultrasound in breast cancer screening. A consensus statement by the European Group for Breast Cancer Screening," Eur. J Cancer, vol. 34, no. 4, pp. 449-450, Mar. 1998.
[6] M.-F. Hou et al., "Comparison of breast mammography, sonography and physical examination for screening women at high risk of breast cancer in taiwan," Ultrasound in Medicine and Biology, vol. 28, no. 4, pp. 415-420, Apr. 2002.
[7] D.M. Agnese, "Advances in breast imaging," Surg. Technol. Int., vol. 14 pp. 51-56, 2005.
[8] C. Kimme-Smith, P.A. Rothschild, L.W. Bassett, R.H. Gold, and D. Westbrook, "Ultrasound artifacts affecting the diagnosis of breast masses," Ultrasound Med. Biol., vol. 14 Suppl 1 pp. 203-210, 1988.
[9] R. Lagalla and M. Midiri, "Image quality control in breast ultrasound," European Journal of Radiology, vol. 27, no. 2, pp. 229-233, May 1998.
[10] J.A. Baker and M.S. Soo, "Breast US: assessment of technical quality and image interpretation," Radiology, vol. 223, no. 1, pp. 229-238, Apr. 2002.
[11] L.A. Zadeh, "Fuzzy Sets," Information and Control, vol. 8 pp. 338-353, 1965.
[12] L.A. Zadeh, "Information granulation and its centrality in human and machine intelligence," in Systems, Man, and Cybernetics, 1997.'Computational Cybernetics and Simulation'., 1997 IEEE International Conference on, 1997, pp. 486-487.
[13] Y.C. Lynn and K.K. Hon, "Fuzzy classifications using fuzzy inference networks," IEEE Trans. Systems, Man and Cybernetics, Part B, vol. 28, no. 3, pp. 334-347, 1998.
[14] P. Sincak, M. Hric, and R. Val'o, "Fuzzy clusters identification in the feature space using neural networks," in Neural Networks, 2001.Proceedings.IJCNN '01.International Joint Conference on, 2001, pp. 1368-1373.
[15] Li-Xin Wang, A course in fuzzy systems and control Prentice Hall, 1997.
[16] C. Schuh, "Fuzzy sets and their application in medicine," in Fuzzy Information Processing Society, 2005.NAFIPS 2005.Annual Meeting of the North American, 2005, pp. 86-91.
[17] M.A. Wirth, J. Lyon, and D. Nikitenko, "A fuzzy approach to segmenting the breast region in mammograms," in Fuzzy Information, 2004.Processing NAFIPS '04.IEEE Annual Meeting of the, 2004, pp. 474-479.
[18] K.K. Lindfors, M.C. McGahan, C.J. Rosenquist, and G.S. Hurlock, "Computer-aided detection of breast cancer: a cost-effectiveness study," Radiology, Mar. 2006.
[19] T. Tanaka, K. Miwa, and S. Kanda, "Application of fuzzy reasoning in an expert system for ultrasonography," Dentomaxillofac. Radiol., vol. 26, no. 2, pp. 125-131, Mar. 1997.
[20] L. Jeanpierre and F. Charpillet, "Automated medical diagnosis with fuzzy stochastic models: monitoring chronic diseases," Acta Biotheor., vol. 52, no. 4, pp. 291-311, 2004.
[21] A.M. Chiang, P.P. Chang, and S.R. Broadstone, "PC-based ultrasound imaging system in a probe," in Ultrasonics Symposium, 2000 IEEE, 2000, pp. 1255-1260.
[22] A.M. Chiang, "PC-based ultrasound imaging system with microminiaturized electronics," Ultrasound in Medicine and Biology, vol. 29, no. 5, pp. 86-87, May 2003.
[23] Teratech Corp., http://www.terason.com 2006.
[24] R.N. Czerwinski, D.L. Jones, and W.D. O'Brien, "Detection of lines and boundaries in speckle images - Application to medical ultrasound," IEEE Trans. Med. Imaging, vol. 18, no. 2, pp. 126-136, Feb. 1999.
[25] R.N. Czerwinski, D.L. Jones, and W.D. O'Brien, Jr., "Edge detection in ultrasound speckle noise," in Proc. IEEE Int. Conf. Image Processing, Austin, TX, 1994, pp. 304-308.
[26] C. Xiang-Yong, A. Ohya, M. Natori, and M. Nakajima, "Boundary extraction method for three dimensional ultrasonic echo imaging using fuzzy reasoning and relaxation techniques," in Nuclear Science Symposium and Medical Imaging Conference, 1993., 1993 IEEE Conference Record., 1993, pp. 1610-1614.
[27] Francis A.Duck, Andrew C.Baker, and H.C.Starritt, Ultrasound in Medicine Inst of Physics Pub, 1998.
[28] "Breast Ultrasound. 2nd ed," Radiology, vol. 222, no. 1, pp. 156, Jan. 2002.
[29] C.L. Rapp, "Sonography of the breast," in Proceedings of Society of Diagnostic Medical Sonography's 17th Annual Conference in Proceedings of Society of Diagnostic Medical Sonography's 17th Annual Conference, Dallas, Texas, 2003, pp. 57-67.
[30] America College of Radiology, "ACR standards 2000-2001," Restion Va, American College of Radiology, 2000.
[31] T. Zhang, Xu X., and Chen X., "A Software Package for Portable Three-Dimensional Ultrasound Imaging," in IEEE International Symposium on Biomedical Imaging, Arlington, VA, USA, 2004, pp. 539-542.
[32] R.C. Gonzalez and R.E. Woods, Digital Image Processing , Second ed. Reading, MA: Addison-Wesley, 1992.
[33] R.F. Wagner, M.F. Insana, and S.W. Smith, "Fundamental correlation lengths of coherent speckle in medical ultrasonic images," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 35, no. 1, pp. 33-44, Jan. 1988.
[34] Haralick R.M., "Digital step edges from zero crossing of second directional derivatives," Ieee Trans. Pattern Anal. Mach. Intell., vol. 6, no. 1, pp. 58-68, Jan. 1984.
[35] S. Shimizu, T. Kondo, T. Kohashi, M. Tsurata, and T. Komuro, "A new algorithm for exposure control based on fuzzy logic for video cameras," Consumer Electronics, IEEE Transactions on, vol. 38, no. 3, pp. 617-623, 1992.
[36] X.Y. Cheng et al., "Breast tumor diagnosis system using three dimensional ultrasonic echography," in Engineering in Medicine and Biology society, 1997.Proceedings of the 19th Annual International Conference of the IEEE, 1997, pp. 517-520.
[37] A. Rosenfeld and J.L. Pfaltz, "Sequential operations in digital processing," JACM, 1966.
[38] H.C. Thomas, Introduction to Algorithms MIT Press, 2001.
[39] J. A. Baker, "Artifacts and Pitfalls in Sonographic Imaging of the Breast," Am. J. Roentgenol., vol. 176 pp. 1261-1266, 2001.
[40] D.R. Chen, R.F. Chang, W.J. Wu, W.K. Moon, and W.L. Wu, "3-D breast ultrasound segmentation using active contour model," Ultrasound Med. Biol., vol. 29, no. 7, pp. 1017-1026, July 2003.
[41] W.M. Chen, R.F. Chang, W.K. Moon, and D.R. Chen, "Breast cancer diagnosis using three-dimensional ultrasound and pixel relation analysis," Ultrasound Med. Biol., vol. 29, no. 7, pp. 1027-1035, July 2003.
[42] D.R. Chen et al., "Texture analysis of breast tumors on sonograms," Semin. Ultrasound CT MR, vol. 21, no. 4, pp. 308-316, Aug. 2000.
[43] D.R. Chen, R.F. Chang, W.J. Kuo, M.C. Chen, and Y.L. Huang, "Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks," Ultrasound Med. Biol., vol. 28, no. 10, pp. 1301-1310, Oct. 2002.
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