臺灣博碩士論文加值系統

在這篇論文裡面，我們應用主動尋求輪廓模型克服超音波影像的自然特性-斑點，雜訊及組織紋理以精確的切割出腫瘤輪廓。在輪廓變形階段之前，一個好的最初輪廓是需要的而它將會使切出來腫瘤輪廓更接近實際輪廓。然而，手繪最初輪廓非常花時間，我們提出了一種自動找出最初輪廓的方法並使得找出的最初輪廓仍維持腫瘤的形狀且會很接近腫瘤的邊線並存在於腫瘤的內部。在輪廓變形階段中，為防止輪廓不會陷入斑點和組織紋理所造成的錯誤位置，我們將邊緣資訊加入到影像特徵裡去定義外力。因為三維腫瘤體積可以由一連串二維影像所組成的，所以我們的切割技術可以延伸到三維影像上面。當切割出三維影像的腫瘤輪廓後，便可以利用這些資訊來算出腫瘤的體積。最後，我們把算出來的體積跟醫生求得的體積做比較，並得到令人滿意的結果。

In this paper, we make use of the discrete active contour model to overcome the natural properties of ultrasound images, speckles, noise, and tissue-related textures, to segment the breast tumors precisely. Before the snake deformation process, a good initial contour is needed and it will make the final contour as close as to the real tumor boundary. However, the manual way to sketch an initial contour is very time-consuming. Thus, we propose an automatic initial contour finding method, which not only maintains the tumor shape but also is close to the tumor boundary and inside the tumor. During the deformation process, in order to prevent the snake trapping into the false position caused by tissues-related textures or speckles, we add the edge information as an image feature to define the external force. Besides, because the three-dimensional volume of a tumor is essentially constructed by a sequence of two-dimensional images, our method for finding boundaries of a tumor can be extended to three-dimensional cases. By precisely counting the volume of the three-dimensional images, we can get the volume of tumor. Finally, we will show that the proposed techniques have rather good performance and lead to satisfactory result in comparison between the estimated volume and physicians.

摘 要i
ABSTRACTii
ACKNOWLEDGEMENTSiv
TABLE OF CONTENTSv
LIST OF FIGURESvii
LIST OF TABLESx
Chapter 1 Introduction1
Chapter 2 3D Ultrasound, Snake, and Stick Techniques4
2.1Three-dimensional Ultrasound Mammography4
2.1.1 Volume Measurement7
2.1.2 Volume Of Interest9
2.2The Discrete Dynamic Contour Model11
2.2.1 Three Principle Forces11
2.2.2 Vertex Resampling13
2.3Stick Detection Method14
Chapter 3 Automatic Tumor Segmentation and Volume Measurement15
3.1Initial Contour Finding Method15
3.2The Modified Snake Model19
3.3Fast Contour Finding Method21
3.4Contour Detection On Three-dimensional Volume22
Chapter 4 Experiment Results And Discussion25
Chapter 5 Conclusion28
References29

[1]J. W. Gurney, “Neural networks at the crossroads: Caution ahead,” Radiology, vol. 193, no. 1, pp. 27-28, Oct. 1994.
[2] J. M. Boone, “Neural networks at the crossroads,” Radiology, vol. 189, no. 2, pp. 357-359, Nov. 1993.
[3] M. L. Astion and P. Wilding, “The application of backpropagation neural networks to problems in pathology and laboratory medicine,” Arch. Pathol. Lab. Med., vol. 116, pp. 995-1001, 1992.
[4] D. R. Chen, R. F. Chang, Y. L. Huang, “Computer-aided diagnosis applied to US of solid breast nodules by using neural networks,” Radiology, vol. 213, no. 2, pp. 407-412, 1999.
[5] D. R. Chen, R. F. Chang, Y. L. Huang, “Breast cancer diagnosis using self-organizing map for sonography,” Ultrasound in Med. & Biol., vol. 26, no. 3, pp. 405-411, Mar. 2000.
[6] D. R. Chen, R. F. Chang, Y. L. Huang , Y. H. Chou, C. M. Tiu, and P. P. Tsai, “Two scan planes texture analysis of breast tumors on sonograms,” Seminars in ultrasound CT and MRI, vol. 21, no. 4, pp. 308-316, Mar. 2000.
[7] M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models,” in Proc. Int’l Conf. Computer Vision, London, England, June 1987, pp. 259-268.
[8] D. Terzopoulos, A. Witkin, and M. Kass, “Symmetry-seeking models for 3D object reconstruction,” in Proc. Int’l Conf. Computer, Vision, London, Englang, June 1987, pp. 269-276.
[9] D. T. Kuan and et al., “Adaptive restoration of images with speckle,” IEEE Trans. Acoust. Speech, Signal Proc., vol. 35, no.3, pp. 373-383, 1987.
[10]A. N. Evans and M. S. Nixon, “Biased motion-adaptive temporal filtering for speckle reduction in echocardiography,” IEEE Trans. Med. Imag., vol. 15, no. 1, pp. 39-50, 1996.
[11]F. Lefebvre, G. Berger, and P. Laugier, “Automatic detection of the boundary of the calcaneus from ultrasound parametric images using an active contour model; Clinical assessment,” IEEE Trans. Med. Imag., vol. 17, no. 1, pp. 45-52, Feb. 1998.
[12]Y. S. Akgul, C. Kambhamettu, and M. Stone, “Extraction and tracking of the Tongue surface from ultrasound image sequences,” in Proc. IEEE Int. Computer Society Conf. Computer Vision and Pattern Recognition, Santa Barbara, California, June 1998, pp. 298-303.
[13]R. Chung and C. K. Ho, “Using 2D active contour models for 3D reconstruction from serial sections,” in Proc. the 13th Int. IEEE Conf. on Pattern Recognition, Vienna, Austria, Aug. 1996, pp. 849-853.
[14]A. Fenster, S. Tong, H. N. Cardinal, C. Blake, and D. B. Downey, “Three-dimensional ultrasound imaging system for prostate cancer diagnosis and treatment,” IEEE Trans. Instrumentation and Measurement, vol. 47, no. 6, pp. 1439-1447, Dec. 1998.
[15]M. G. Strintzis and I. Kokkinidis, “Maximum likelihood motion estimation in ultrasound image sequences,” IEEE Trans. Signal Process., vol. 4, no. 6, pp. 156-157, June 1997.
[16]L. D. Cohen and I. Cohen, “Finite-element methods for active contour models and balloons for 2D and 3D images,” IEEE Trans. Pattern Anal. Machine Intell., vol. 15, no. 11, pp. 1131-1147. Nov. 1993.
[17]M. Riccabona, T. R. Nelson, D.H. Pretorius., “Ultrasound Obstet,” Gynecol, pp. 429-434, 1996.
[18]C. Roux, J. L. Coatrieux, Contemporary Perspectives in Three-Dimensional Biomedical Imaging. Amsterdan: IOS Press, 1997.
[19]S. Lobregt and M. A. Viergever, “A discrete dynamic contour model,” IEEE Trans. Med. Imag., vol. 14, no. 1, pp. 12-24, Mar. 1995.
[20]N. Richard and R. N. Czerwinski, “Detection of lines and boundaries in speckle images-application to medical ultrasound,” IEEE Trans. Med. Imag., vol. 18, no. 2, pp. 126-136, Feb. 1999.
[21]R. C. Gonzales and P. Wintz, Digital Image Processing. 2nd Ed., Reading, Massachusetts: Addison-Wesley, 1987.
[22]N. Ramesh, J.-H. Yoo, and I. K. Sethi, “Threshholding based on histogram approximation,” IEE Proc.-Vis Image Signal Process., vol. 142, no. 5, pp 271-279, Oct. 1995.
[23]N. Otsu, “A threshold selection method from gray-level histogram,” IEEE Trans. Syst., Man Cybern., vol. 9, no. 1, pp. 62-66, Jan. 1979.