پایان نامه جداسازی پروجکشن های پروتز و ایمپلنت های بافت در تصاویر ساینوگرام سی تی اسکن اسپایرال با استفاده از روش های کانتور فعال

فهرست و منابع پایان نامه جداسازی پروجکشن های پروتز و ایمپلنت های بافت در تصاویر ساینوگرام سی تی اسکن اسپایرال با استفاده از روش های کانتور فعال

فهرست:

فهرست مطالب

 

 

عنوان                                                                                                     صفحه

 

1- فصل اول: مقدمه                                                                                                 1

1-1- مقدمه                                                                                      2

1-2-  Filtered Back Projection (FBP) و آرتیفکت­ها در سی تی                       4

1-2-1- سخت شدن پرتوها                                                                 6

1-2-2- کمبود فوتون                                                                       7

1-2-3- پراکندگی                                                                           7

1-2-4- اثر حجم نسبی                                                                     7

1-3- اهداف کلی این پایان نامه                                                                8

2- فصل دوم: مبانی نظری تحقیق                                                                             9

2-1- تاریخچه سی تی اسکن                                                                10

2-2- اجزای اصلی تشکیل دهنده دستگاه سی تی اسکن                                 11

2-3- نسل­های مختلف دستگاه سی تی اسکن تا به امروز                                 14

2-3-1-  نسل اول                                                                         15

2-3-2-  نسل دوم                                                                         16

2-3-3-  نسل سوم                                                                        17

2-3-4-  نسل چهارم                                                                      17

2-3-5-  نسل پنجم، سی­تی اسکنهای EBCT                                         18

2-3-6-  نسل ششم، سی تی اسکن­های مارپیچ (اسپایرال یا هلیکال)                19

2-3-7-  نسل هفتم، سی تی اسکنهای مالتی اسلایس                                20

2-4- الگوریتم­های بازسازی تصویر                                                           21

2-4-1- ساینوگرام                                                                         23

2-4-2-  الگوریتم ART                                                                  27

2-4-3-  الگوریتم Back Projection Fourier Slice                                   28

2-4-4-  الگوریتم Filtered Back Projection Fourier Slice                        32

2-5- بازسازی تصویر در حالت پرتو بادبزنی شکل                                          34

2-5-1- بازسازی تصویر در حالت پرتو بادبزنی با زاویه­های مساوی                    35

2-5-2- بازسازی تصویر در حالت پرتو بادبزنی با فضاهای پوششی مساوی            38

2-6- بازسازی تصویر در سی­تی­های نسل اسپایرال                                         38

2-7- کانتورهای فعال                                                                         40

2-7-1- مدل­های اکتیو کانتور پارامتریک                                                41

2-7-2- مدل­های کانتور فعال هندسی                                                   53

2-8- تبدیل هاف                                                                              62

3- فصل سوم: مروری بر تحقیقات انجام شده                                                        68

4- فصل چهارم: روش تحقیق                                                                                 75

4-1- مقدمه                                                                                    76

4-2- روش تصویر برداری مارپیچ و مقایسه آن با اسکنر معمولی                          77

4-3- تشکیل ساینوگرام                                                                      78

4-4- الگوریتم مطرح شده                                                                    79

4-4-1- مدل کانتور فعال                                                                 80

4-4-2- الگوریتم بر پایه تبدیل هاف                                                     84

4-4-3- الگوریتم پس از پردازش                                                         86

4-5- نتیجه گیری                                                                             86

5- فصل پنجم: نتایج                                                                                              88

5-1- اعمال الگوریتم پیشنهادی بر داده­های واقعی و مقایسه به صورت شهودی        89

5-2- اعمال الگوریتم پیشنهادی بر مولاژ و مقایسه به صورت کمی                       90

5-2-1- میانگین و واریانس سطح خاکستری                                           92

5-2-2- میانگین مربع خطاها                                                             93

5-2-3- نسبت ماکزیمم سیگنال به نویز                                                 93

5-2-4- پارامتر Q                                                                         94

5-3- مقایسه الگوریتم مطرح شده با روش جداسازی با حد آستانه و تبدیل هاف     119

5-4- نتیجه گیری                                                                           122

مراجع                                                                                                                  123

منبع:

[1] Hsieh, J. (2009), Computed Tomography Principles, Design, Artifacts, and Recent Advances, 2nd ed. Washington: Bellingham.

[2] Yazdi, M., and Beaulieul, L. (2007). "Artifacts in Spiral X-ray CT Scanners: Problems and Solutions," Proceeding of World Academy of Science, Engineering and Technology 26: 376-380.

[3] DeMan, B. "Iterative Reconstruction for Reduction of Metal Artifacts in Computed Tomography," PhD. Dissertation, Katholieke Universiteit Leuven, Belgium, 2001.

[4] Xu, C., Pham, D.L., and Prince, J.L. (2000). "Medical Image Segmentation Using Deformable Models," Handbook of Medical Imaging, Volume 2: Medical Image Processing and Analysis, PP.129-174, edited by Fitzpatrick, J.M., and Sonka, M., SPIE Press.

[5] Xu, C., and Prince, J.L. "Gradient Vector Flow: A New External Force for Snakes," Proc. IEEE Conf. on CVPR, Los Alamitos Comp. Soc. Press, PP.66-71, June 1997.

[6] عزیزی، امیر، تبدیل هاف، پژوهشکده­ی الکترونیک دانشگاه علم و صنعت ایران: گروه ماشین بینایی و پردازش تصویر، آذر 1389، [Online]، .

[7] Zamyatin, A. "Method, Apparatus, and Computer Program Product for Sinogram Completions". Patent Number: 7515676, Patent Date: Apr. 7, 2009.

[8] Zamyatin, A.A., and Nakanishi, S. (2006). “Sinogram Correction Methods using Sinogram Decomposition,” IEEE Nuclear Science Symposium Conference Record, vol.6, pp.3438-3440.

[9] Changchun, Z., and Ge, S. "A Hough Transform-Based Method for Fast Detection of Fixed Period Sinusoidal Curves in Images," in Proceedings of the 6th International Conference on Signal Processing, vol. 1, 2002, pp. 909-912, Aug. 26-30.

[10] Ballard, D.H. (1987). "Generalizing the Hough Transform to Detect Arbitrary Shapes," Readings in Computer Vision: issues, problems, principles, and paradigms, pp. 714-725.

[11] Ebraheim, N.A., Coombs, R., and Rusin, J.J. (1990). "Reduction of Postoperative CT Artifacts of Pelvis Fractures by Use of Titanium Implants," Orthopedics, vol. 13, pp. 1357-1358.

[12] Kalender, W., Hebel, R., and Ebersberger, J. (1987). "Reduction of CT Artifacts Caused by Metallic Implants," Radiology, vol. 164, No. 2, pp. 576-577.

[13] Klotz, E., and Kalender, W. (1990). "Algorithms for the Reduction of CT Artifacts Caused by Metallic Implants," Medical Imaging, vol. 1234, pp. 642-650.

[14] Liu, J., Watt-Smith, S., and Smith, S. (2003). "CT Reconstruction Using FBP with Sinusoidal Amendment for Metal Artifact Reduction," Proceeding of 7th Digital Image Computing: Techniques and Applications, pp. 439-447.

 [15] Mahnken, A.H., Raupach, R., and Wildberger, J.E. (2003). "A New Algorithm for Metal Artifact Reduction in Computed Tomograghy: In Vitro and in Vivo Evaluation after Total Hip Replacement," Invest Radiol, vol. 38, pp. 769-775.

[16] Yazdi, M., Gingras, L., and Beaulieu, L. (2005). “An Adaptive Approach of Metal Artifact Reduction in Helical CT for Radiation Therapy Treatment Planning: Experimental and Clinical Studies” Int. J. Radiat. Oncol., Biol., Phys. 62, 1224-1231.

[17] Yazdi, M., and Beaulieu, L. (2006). “A novel approach for reducing metal artifacts due to metallic dental implants,” IEEE Nuclear Science Symposium Conference Record, vol. 4, pp. 2260–2263.

[18] H.X Xue, L.Zhang, Y.Xiao, Z.Chen, and Y.Xing" Metal Artifact Reduction in Dual Energy CT by Sinogram Segmentation Based on Active Contour Model and TV Inpainting," IEEE Nuclear Science Symposium Conference Recor, pp.904-908, Oct. 2009.

[19] Chanand, T., and Vese, L. “Active contours without edges,” IEEE Trans. Image Process. vol. 10, no. 2, pp. 266–277, Feb. 2001.

[20] Veldkamp, W.J., Joemai, R.M., Van der Molen, A.J., and Geleijns, J. “Development and validation of segmentation and interpolation techniques in sinograms for metal artifact suppression in CT,” Med. Phys, vol.37, pp. 620-628, Feb.2010.

[21] Abdoli, M., Ay, MR., and Ahmadian, A. “A virtual sinogram method to reduce dental metallic implant artefacts in ct-based attenuation correction for pet” Nuclear Medicine Communications, pp.22-31, Jan.2010.

[22] Kim, Y., Yoon, S., and Yi, J. “Effective sinogram-inpainting for metal artifacts reduction in X-ray CT images,” IEEE International Conference on Image Processing, pp.597-600, Sep.2010.

[23] Li, C., Huang, R., Ding, Z., Gatenby, J., Metaxas, D., and Gore, J. “A level set method for Image segmentation in the presence of intensity inhomogeneities with application to mri” IEEE Trans. Image Process., vol. 20, no. 7, July 2011.

[24] Li, C., Kao, C., Gore, J. C., and Ding, Z. “Minimization of region-scalable fitting energy for image segmentation,” IEEE Trans. Image Process., vol. 17, no. 10, pp. 1940–1949, Oct. 2008.

[25] Mumford, D., and Shah, J. “Optimal approximation by piecewise smooth functions and associated variational problems,” Commun. Pure Appl. Math, vol. 42, pp. 577–685, 1989.

[26] Li, C., Xu, C., Gui, C., and Fox, M. D. (2005). “Level set evolution without re-initialization: A new variational formulation,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 430–436.

[27] Shnayderman, A., Gusev, A., and Eskicioglu, A. “A multidimensional image quality measure using Singular Value Decomposition.” In: Proc. SPIE Image Quality and Image Quality and System Performance, vol. 5294, pp. 82-92. (2003).

[28]Wang, Zh. (2002). “A Universal Image Quality Index” IEEE Signal Processing Letters, vol. 9, No. 3, pp. 81-84.