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研究生:侯玉翎
研究生(外文):Yu-Ling Hou
論文名稱:智慧型演算法用於乳房病變之超音波影像辨識
論文名稱(外文):Intelligent-Based Algorithm in Classifying Breast Nodules of Ultrasound Image
指導教授:林志民林志民引用關係
指導教授(外文):Chih-Min Lin
學位類別:碩士
校院名稱:元智大學
系所名稱:電機工程學系
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:2007
畢業學年度:95
語文別:英文
論文頁數:139
中文關鍵詞:超音波影像斑點雜訊類神經網路小腦模型
外文關鍵詞:ultrasound (US) imagespeckleneural network (NN)cerebellar model articulation computer (CMAC)
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好的電腦輔助診斷系統可以幫助經驗不足的醫生避免誤判和在不漏掉惡性腫瘤的情形下減少良性組織的切片檢查。已經有很多用在醫學影像上的電腦輔助診斷系統被提出。然而,仍有很多人對這些系統抱著不信任的態度。無論有多少人感到懷疑,電腦補助診斷系統仍然持續發展中,並且被應用在很多臨床上的醫學影像領域。在本論文中,我們將針對超音波電腦補助診斷系統進行研究。我們致力於找出一個高效率的分類器。我們比較類神經網路和小腦模型的效率,並且提出結合類神經網路和小腦模型的網路架構。最後,可以發現多階層網路效率優於類神經網路與小腦模型。
最後,因為在女性癌症死亡原因中,乳癌的排名逐漸往前提升,因此一套用來幫助醫生能夠早期偵測癌症的電腦補助診斷系統變得越來越重要。最後的實驗結果顯示,本研究所使用的這些腫瘤特徵與分類架構,能有效地應用在超音波影像的腫瘤判斷上,且得到不錯的乳房腫瘤診斷之正確率可達96%,高於醫師人為判斷的正確率(約88%),這可幫助臨床醫師使之能做出正確的判斷,更能節省醫師診斷所花費的時間,以達到早期發現早期治療的目標。
A well-designed computer-aided diagnosis (CAD) system can assist physicians to avoid misdiagnosis and reduce the number of benign lesion biopsies with missing cancers. There have been many successful applications of CAD strategy reported for ultrasound image. However, many people were still skeptical about these CAD systems in the past. No matter how skeptical these people may have been, CAD is still undergoing great development and utilization within the field of medical imaging as its potentials are well demonstrated in many clinic areas. In this thesis, we will explore the ultrasound CAD system and aim for a high performance classifier with intelligent algorithm. We will compare the performance of the neural network (NN) and cerebellar model articulation computer (CMAC). We also merge NN and CMAC to a new structure called intelligent hierarchical network (HN). The new intelligent HN understands the advantages of NN and CMAC. Finally, the performance of HN is proven to be superior to NN and CMAC.
Lastly, since the mortality rate of breast cancer in women is gradually increasing, CAD system helps in the early diagnosis of cancer by doctors using CAD system. The simulations demonstrate that the proposed CAD system can differentiate breast nodules with relatively high accuracy (96%); it is higher than the operator’s diagnosis (88%).So that it can help operators to avoid misdiagnosis.
Contents

Chinese Abstract i
Abstract ii
Acknowledgement iii
Contents iv
List of Tables vi
List of Figures viii
Nomenclature xiii

1. Introduction
1.1 General Remark and Overview of Previous Work 1
1.2 Motivation and Organization of the Thesis 6

2. Automatic Ultrasound Breast Tumor Segmentation Based on Texture and Shape Information
2.1 Image Information 10
2.1.1 Speckle noise 10
2.1.2 Image data acquisition system 12
2.2 Texture Feature Analysis 13
2.2.1 Auto-covariance coefficient 13
2.2.2 Spatial gray-level dependence matrix 15
2.3 Shape Feature Analysis 17
2.3.1 Techniques overview 17
2.3.2 Shape contour extraction 20
2.3.3 Results analysis 33
2.3.4 Shape feature extract 36
2.4 Summary 37

3. Ultrasound Images Classification of Breast Tumors by Using Neural Networks
3.1 Overview of Neural Network 50
3.2 Neural Network Structure 53
3.3 Experimental Results 57
3.4 Summary 60

4. Ultrasound Images Classification for Breast Tumors by Using Cerebellar Model Articulation Computer
4.1 Overview of Cerebellar Model Articulation Computer 71
4.2 Cerebellar Model Articulation Computer Structure 78
4.3 Experimental Results 83
4.4 Summary 85

5. Ultrasound Images Classification for Breast Tumors by Using Hierarchical Networks
5.1 The Structure of Hierarchical Network 101
5.2 Experimental Results 104
5.3 Summary 108

6. Conclusions and Suggestions for Future Research
6.1 Conclusions 129
6.2 Suggestions for Future Research 131

Reference 134
Autobiography 139
[1]Department of Health, Executive Yuan, R.O.C. TAIWAN (2006). Cause of Death Statistics. Retrieved March 26, 2006, from http://www.doh.gov.tw/statistic/index.htm
[2]S. Joo, Y. S. Yang, W. K. Moon and H. C. Kim, ‘‘Computer-aided diagnosis of solid breast nodules: use of artificial neural network based on multiple sonographic features, ’’ IEEE Trans. on Med. Imag., vol. 23, no. 10, pp. 1292–1300, 2004.
[3]J. W. Goodman, “Some fundamental properties of speckle,” J. Opt. Soc. Amer. ,vol. 66, no. 11, pp. 1145–1150, 1976.
[4]C. B. Burckhardt, “Speckle in ultrasound B-mode scans,” IEEE Trans. on Sonics Ultrason., vol. 25, no. 1, pp. 1–6, 1978.
[5]R. F. Wagner, S. W. Smith, J. M. Sandrik, and H. Lopez, “Statistics of speckle in ultrasound B-scans,” IEEE Trans. on Sonics Ultrason., vol. 30, no. 3, pp. 156–163, 1983.
[6]J. G. Abbott and F. L. Thurstone, “Acoustic speckle: Theory and experimental analysis,” Ultrason. Imag., vol. 1, no. 4, pp. 303–324, 1979.
[7]J. G. Thomas, R. A. Peters, and P. Jeanty, “Automatic segmentation of ultrasound images using morphological operatots,” IEEE Trans. on Med. Imag., vol. 10, no. 2, pp. 180–186, 1991.
[8]C. Kotropoulos, X. Magnisalis, I. Pitas, and M. G. Strintzis, “Nonlinear ultrasonic image processing based on signal-adaptive filters and self-organizing neural networks,” IEEE Trans. on Image Processing, vol. 3, no. 1, pp. 65–77, 1994.
[9]J. Feng, W. Lin and C. Chen, “Epicardial boundary detection using fuzzy reasoning,” IEEE Trans. on Med. Imag., vol. 10, no. 2, pp. 187–199, 1991.
[10]S. K. Satarehdan, and J. J. Soraghan, “Automatic cardiac LV boundary detection and tracking using hybrid fuzzy temporal and fuzzy multiscale edge detection,” IEEE Trans. on Biomed. Eng., vol. 46, no. 11, pp. 1364–1378, 1999.
[11]R. Hu, and M. M. Fahmy, “Texture segmentation based on a hierarchical Markov random field model,” Signal Process. vol. 26, no.3, pp. 285-305, 1992.
[12]C. C. Chen, J. S. Daponte and M. D. Fox, “Fractal feature analysis and classification in medical imaging,“ IEEE Trans. on Med. Imag., vol. 8, no. 2, pp. 133-142, June 1989.
[13]Y. Chen, E. R. Dougherty S. M. Totterman and J. P. Hornak ,“Classification of trabecular structure in magnetic resonance images based on morphological granulometries, ” Magn. Reson. Med., vol. 29, no. 3, pp. 358-370, Mar. 1993.
[14]L. Estevez, N. Kehtarnavaz and R. Wendt Ⅲ “Interactive selective and adaptive clustering for detection of microcalclifications in mammograms,” Digital Signal Processing/ A Review Journal, vol. 6, no. 4, pp. 224-232, 1996.
[15]D. C. Young, Y. S. Sang, and C. K. Nam, “ Image retrieval using BDIP and BVLC moments,” IEEE Trans. on Circuits and Syst. for Video Technology, vol. 13, no. 9, pp. 951-957, 2003.
[16]W. M. Chen, R. F. Chang, S. J. Kuo, C. S. Chang, W. K. Moon, S. T. Chen et al., “ 3-D ultrasound texture classification using run difference matrix, ” Ultrasound Med. Biol., vol. 31, no. 6, pp. 763-770, June 2005.
[17]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.
[18]C. I. Christodoulou, C. S. Pattichis, M. Pantziaris, and A. Nicolaides, “Texture-based classification of atherosclerotic carotid plaques,” IEEE Trans. on Med. Imag., vol. 22, no. 7, pp. 902-912, 2003.
[19]M. Amadasun and R. King, “Textural features corresponding to textural properties,” IEEE Trans. on Syst., Man and Cybernetics, vol. 19, no.5, pp. 1264-1274, 1989.
[20]X. Hao , S. Gao , , X. Gao , “A novel multiscale nonlinear thresholding method for ultrasonic speckle suppressing, ” IEEE Trans. on Med. Imag., Vol. 18, no. 9, pp. 787 – 794 Sept. 1999.
[21]R. G. Aarnink, R. J. Giesen, A. L. Huynen, J.J. de la Rosette, F. M. Debruyne, and H. Wijkstra, “A practical clinical method for contour determination in ultrasonographic prostate images,” Ultrasound Med. Biol., vol. 20, no. 8, pp. 705–717, 1994.
[22]F. Zhao, C. J. S. de Silva, “Use of the laplacian of gaussian operator in prostate ultrasound image processing,” in Proc. IEEE Int. Conf. Engineering in Medicine and Biology, vol. 20, no. 2, pp. 812–815, 1998.
[23]X. Hao, C. Bruce, C. Pislaru, and J. F. Greenleaf, “A novel region growing method for segmenting ultrasound images,” in Proc. IEEE Ultrason. Symp, vol. 2, pp. 1717–1720, 2000.
[24]Y. Chen, R. Yin, P. Flynn, and S. Broschat, ”Aggressive region growing for speckle reduction in ultrasound images,” Pattern Recognition Letters Volume: 24, Issue: 4-5, February, 2003, pp. 677-691.
[25]M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models,” Int. J. Comput. Vis., vol. 1, no. 4, pp. 321–331, 1987.

[26]C. Davatzikos, and R. Bryan, “Using a deformable surface model to obtain a shape representation of the cortex,” IEEE Trans. on Med. Imag., vol. 15, no. 6, pp.785–795, 1996.
[27]R. Muzzolini, Y. H. Yang, and R. Pierson, “Multiresolution texture segmentation with application to diagnostic ultrasound images,” IEEE Trans. on Med. Imag., vol.12, no. 1, pp. 108–123, 1993.
[28]V. Chalana, D. Linker, D. Haynor, and Y. Kim, “A multiple active contour model for cardiac boundary detection on echocardiographic sequences,” IEEE Trans. on Med. Imag., vol. 15, no. 3, pp. 290–298, 1996.
[29]C. Xu and J. L. Prince, “Gradient vector flow: A new external force for snakes,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 66–71, 1997.
[30]C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Trans. on Image Process. vol. 7, no. 3, pp. 359–369, 1998.
[31]A. T. Stavros, D. Thickman, C. L. Rapp M .A. Dennis, S. H. Parker, and G. A. Siseny, ‘‘Solid breast nodules-use of sonography to distinguish benign and malignant lesions,” Radiology, vol. 196, no. 1, pp. 123–134, July.1995.
[32]D. R. Chen, W. J. Kuo, R. F. Chang, W. K. Moon, and C. C. Lee, “Use of the bootstrap technique with small training sets for computer-aided diagnosis in breast ultrasound,” Ultrasound Med. Biol., vol. 28, pp. 897–902, 2002.
[33]W. J. Kuo, R. F. Chang, C. C. Lee, W. K. Moon, and D. R. Chen, “Re- trieval technique for the diagnosis of solid breast tumors on sonogram,” Ultrasound Med. Biol., vol. 28, pp. 903–909, 2002.
[34]M. L. Giger, H. Al-Hallaq, A. Huo, C. Moran, D. E. Wolverton, C. W. Chan and W. Zhong, “Computerized of lesions in US images of the breast,” Acad. Radiol., vol. 6, pp. 665–674, 1999.
[35]H. K. Chiang, C. M Tiu, G. S. Hung, S. C. Wu, T. Y. Chang, and Y. H. Chou, “Stepwise logistic regression analysis of tumor contour features for breast ultrasound diagnosis,” Ultrason. Symp., vol. 2, pp. 1303–1306, 2001.
[36]C. B. Burckhardt, “Laser speckle pattern – A narrowband noise model,” Bell Syst. Tech. J., vol. 49, pp. 309–316, 1970.
[37]M. Walessa and M. Datcu, “Model-based despeckling and information extraction form SAR images,” IEEE Trans. on Geosci. Remote Sens., vol. 38, no. 5, pp. 2258–2269, 2000.
[38]R. C. Gonzalez and R. E. Woods, “Image compression,” in Digital Image Processing. Reading Massachusetts: Addison-Wesley 1992, pp. 312-315.
[39]R. M. Haralick, K. Shanmugam, I. Dinstein, “Texture Features for Image Classification “, IEEE Trans. on Syst., Man and Cybernetics, Vol. SMC-3, pp. 610-621, Nov. 1973.
[40]V. N. Gudivada andV.V.Raghavan, "Content-based image retrieval systems," Computer, vol. 28, no. 9, pp. 18-22, Sept. 1995.
[41]C. T. Chiang and C. S. Lin, “CMAC with general basis functions,” Neural Networks, vol. 9, no. 7, pp. 1199-1211, 1996.
[42]R. C. Gonzalez and R. E. Woods, Digital Image Processing. New Jersey: Prentice Hall, 2001.
[43]T. Loupas, W. N. Mcdicken, and P. L. Allan, “An adaptive weighted median filter for speckle suppression in medical ultrasonic images,” IEEE Trans. on Circuits Syst., vol. 36, no. 1, pp. 129–135, 1989.
[44]W. N. Lee and P. C. Li, “Strain compounding: Improvement in contour extraction of ultrasonic breast imaging,” J. Med. Biol. Eng., vol. 23, no. 3, pp.103–109, 2003.
[45]A. Madabhushi and D. N. Metaxas, ‘‘Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions,’’ IEEE Trans. on Med. Imag., vol. 22, no. 2, pp. 155–169, 2003.
[46]C. T. Lin and C. S. George Lee, Neural Fuzzy System: A Neuro-Fuzzy Synergism to Intelligent Systems, Englewood Cliffs, NJ: Prentic-Hall, 1996.
[47]K. Nam, “Stabilization of feedback linearizable systems using radial basis function network,” IEEE Trans. on Automatic Control, vol. 44, no. 5, pp. 1026-1031, 1999.
[48]S. S. Ge, C. C. Hang, and T. Zhang, “Adaptive neural network control of nonlinear systems by state and output feedback,” IEEE Trans. on Syst., Man and Cybernetics-Part B: Cybernetics, vol. 29, no. 6, pp. 818-828, 1999.
[49]C. C. Ku and K. Y. Lee, “Diagonal recurrent neural networks for dynamic systems control,” IEEE Trans. on Neural Networks, vol. 6, no. 1, pp. 144-156, 1995.
[50]C. H. Lee and C. C. Teng, “Identification and control of dynamic systems using recurrent fuzzy neural networks,” IEEE Trans. on Fuzzy Syst., vol. 8, no. 4, pp. 349-366, 2000.
[51]L. X. Wang, Adaptive Fuzzy Systems and Control - Design and Stability Analysis, Prentice-Hall, Englewood Cliffs, NJ, pp. 140-154, 1994.
[52]S. N. Singh, W. Yim, and W. R. Wells, “Direct adaptive and neural control of wing-rock motion of slender delta wings,” Journal of Guidance, Control, and Dynamics, vol. 18, no. 1, pp. 25-30, 1995.
[53]F. C. Chen, “Back-propagation neural networks for non-linear self-tuning adaptive control,” IEEE Control Syst. Mag., vol. 51, no. 10, pp.44-48, 1990.
[54]F. J. Lin and R. J. Wai, “Robust control using neural network uncertainty observer for linear induction motor servo drive,” IEEE Trans. on Power Electronics, vol. 17, no. 2, pp. 241-254, 2002.
[55]I. Aleksander and H. Morton, An Introduction to Neural computing. London: Chapman & Hall, 1990.
[56]B. S. Garra, B. H. Krasner, S. C. Ho, S. Ascher, S. K. Mun and R. K. Zeman “Improving the distinction between benign and malignant breast lesions: the value of sonographic texture analysis,” Ultras On. Imaging, vol. 15, no. 4, pp. 267-285, Oct. 1993.
[57]V. Goldberg, A. Manduca, D. L. Ewert, J. J. Gisvold and J. F. Greenleaf “Improvement in specificity of ultrasonography for diagnosis of breast tumors by means of artificial intelligence,” Med. Phys., vol.19 ,no. 6 ,pp. 1475-1481 ,Nov. 1992.
[58]J. S. Albus, “A new approach to manipulator control: The cerebellar model articulation controller (CMAC),” Trans. ASME, J. Dyn. Syst. Meas. Control, vol. 97, no. 3, pp. 220-227, 1975.
[59]S. H. Lane, D. A. Handelman, and J. J. Gelfand, “Theory and development of higher-order CMAC neural networks,” IEEE Control Syst. Mag., vol. 12, no. 2, pp. 23-30, 1992.
[60]J. C. Jan and S. L. Hung, “High-order MS_CMAC neural network,” IEEE Trans. on Neural Networks, vol. 12, no. 3, pp. 598-603, 2001.
[61]R. J. Wai, C. M. Lin, and Y. F. Peng, “Robust CMAC neural network control for LLCC resonant driving linear piezoelectric ceramic motor,” Proc. IEE, Contr. Theory Appl., vol. 150, no. 3, pp. 221-232, 2003.
[62]Y. F. Peng, R. J. Wai, and C. M. Lin, “Implementation of LLCC-resonant driving circuit and adaptive CMAC neural network control for linear piezoelectric ceramic motor,” IEEE Trans. on Ind. Electron., vol. 51, no. 1, pp. 35-48, 2004.
[63]D. Marr, “A theory of cerebellar cortex,” J. Physiol., vol. 202, pp. 437-470, 1969.
[64]J. S. Albus, “A theory of cerebellar function,” Math. Biosci., vol. 10, pp. 25-61, 1971.
[65]D. J. Linden, “Cerebellar long-term depression as investigated in a cell culture preparation,” Behav. Brain Sci., vol. 19, pp. 339-346, 1996.
[66]P. Chauvet and G. A. Chauvet, “Mathematical conditions for adaptive control in Marr’s model of the sensorimotor system,” Neural Networks, vol. 8, no. 5, pp. 693-706, 1995.
[67]M. Ito, The Cerebellum and Neural Control. Raven Press, New York, 1984.
[68]D. O. Hebb, The Organization of Behavior: A Neuropsychological Theory. New York: Wiley, 1949.
[69]M. Ito, “Mechanisms of motor learning in the cerebellum,” Brain Research Interactive, vol. 886, pp. 237-245, 2000.
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