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研究生:蔡銀嬌
研究生(外文):Yin-Chiao Tsai
論文名稱:平均曲度濾波方法於正子斷層掃瞄影像之處理研究
論文名稱(外文):Mean Curvature Diffusion Method for PET Image Processing
指導教授:林康平林康平引用關係
指導教授(外文):Kang-Ping Lin
學位類別:碩士
校院名稱:中原大學
系所名稱:電機工程研究所
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:2002
畢業學年度:90
語文別:英文
論文頁數:56
中文關鍵詞:正子斷層掃瞄濾鏡式反投影疊代式重組平均曲度濾波
外文關鍵詞:filtered backprojectionpositron emission tomographymean curvature diffusioniterative reconstruction
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正子斷層掃瞄(Positron Emission Tomography:PET)是利用人體中正子放射和斷層掃瞄的原理,可以研究人體內的新陳代謝現象,提供活體的生化造影給醫生作為進一步診斷的參考,是近幾年來在核子醫學中一門發展相當快速且嶄新的影像診斷技術。由於造影所收集到的正子放射資料和人體中的生化現象是間接的關係,必須經過逆推的計算方式才能重建人體內的生化造影,目前最常見的影像重建方法有兩種,一種為濾鏡式反投影(Filtered Backprojection:FBP),是一種快速方便的影像重組方式,但是有造成斑紋狀的假影(Streak Artifacts)的缺點;第二種為疊代式重組(Iterative Reconstruction)方法,如ML-EM(Maximum Likelihood-Expectation Maximization),能消除假影改善影像品質,但是因為其反覆分析疊代式之運算需要冗長的計算時間,造成過多記憶體空間的耗費。
本論文提出一種非線性之濾除雜訊技術,平均曲度濾波(Mean Curvature Diffusion:MCD)方法,應用於正子斷層掃瞄之濾鏡式反投影影像重建的處理過程。平均曲度濾波方法在去除雜訊過程中,不但能有效地去除雜訊,且能同時保留影像中組織輪廓的特性,加上濾鏡式反投影之技術,不僅能有效的抑制影像雜訊,更節省了龐大的運算時間。
本論文主要從事於平均曲度濾波方法對於正子斷層掃瞄影像雜訊的處理研究,我們分別於正向投射影像(Sinogram)與濾鏡式反投影之重組影像進行平均曲度濾波方法的處理,我們已於實驗的結果中得到比傳統方法更快速有效的重建影像品質。
Positron Emission Tomography (PET) is a tomographic technique to display metabolic activity in slices through a patient''s body. The popular reconstruction methods today in PET are Filtered Backprojection (FBP) and iterative reconstruction algorithm. FBP is based on a Fourier Transform algorithm and is extremely fast, but the reconstructed image may suffer from annoying streak artifacts. Iterative reconstruction, like Maximum Likelihood-Expectation Maximization (ML-EM) algorithm, depresses the noise problem, but the algorithm is iterated too long, such that the reconstructed image starts to degrade.
In this paper, Mean Curvature Diffusion (MCD), a nonlinear filtering technique, will be applied in the processing of the Filtered Backprojection reconstruction. The Mean Curvature Diffusion approach not only can depress noise but can also reserve the outlines of tissues. Using the combination of Mean Curvature Diffusion and Filtered Backprojection methods, a reconstructed PET image of good quality can be obtained quickly.
In our study, the effect of MCD filtering in depressing the noise of PET image was investigated. The filtering of Mean Curvature Diffusion is applied to both the projection image (sinogram) prior to reconstruction and to the FBP reconstructed image. Preliminary studies on simulating target (phantom) of “sphere-ellipsoid”, we can rapidly get a good reconstructed image by using FBP reconstruction technique and the later MCD filtering method.
目錄
第一章 簡介
1-1. 背景1-1
1-2. 動機1-3
1-3. 全文架構1-4
第二章 平均曲度濾波方法
2-1. 曲面之表示與擴散2-1
2-2. MCD的特性2-2
2-2-1. 邊界之不變性2-2
2-2-2. 穩定性2-2
2-3. 尺度參數與演化速率2-3
2-3-1. 尺度參數2-3
2-3-2. 演化速率2-3
2-4. 影像邊界之維護2-4
2-4-1. 高斯誤差函數2-5
2-4-2. 測試結果2-6
第三章 影像重建
3-1. FBP重建法3-1
3-2. MLEM重建法3-6
3-3. 實驗架構3-10
3-3-1. 卜松雜訊3-11
3-3-2. 雜訊圖譜3-12
第四章 實驗結果
4-1. MCD於卜松雜訊圖譜上之應用4-1
4-2. MCD與FBP於卜松雜訊圖譜之應用4-5
4-3. MLEM於卜松雜訊圖譜之應用4-7
4-4. 結果比較4-11
第五章 結論
Contents
Chapter 1 Introduction
1-1. Background1-1
1-2. Motivation1-3
1-3. Organization1-4
Chapter 2 Mean Curvature Diffusion
2-1. Surface Representation and Diffusion2-1
2-2. Properties of MCD2-2
2-2-1. Edge Invariance2-2
2-2-2. Stability2-2
2-3. The Scaling Parameter and Evolution Speed2-3
2-3-1. Scaling Parameter2-3
2-3-2. Evolution Speed2-3
2-4. Image Edge Preservation2-4
2-4-1. Gaussian Error Function2-5
2-4-2. Performance2-6
Chapter 3 Image Reconstruction
3-1. Filtered Backprojeciton Reconstruction3-1
3-2. Maximum Likelihood-Expectation Maximization Reconstruction3-6
3-3. Experiment Procedure3-10
3-3-1. Poisson Noise3-11
3-3-2. Noisy Sinogram3-12
Chapter 4 Experiment Results
4-1. MCD on Poisson Noisy Sinogram4-1
4-2. MCD with FBP Reconstruction on Poisson Noisy Sinogram4-5
4-3. MLEM Reconstruction on Poisson Noisy Sinogram4-7
4-4. Performance Comparison4-11
Chapter 5 Conclusion
Reference[1]J.M. Ollinger and J.A. Fessler, “Positron-emission tomography,” IEEE Signal Processing Magazine, Vol.14 (1), pp. 43 —55, 1997.[2]O. Demirkaya and E. L. Ritman, “Noise reduction in x-ray microtomographic images by anisotropic diffusion filtration of the scanner projection images,” SPIE Proceedings, vol. 3691-69, 1999.[3]O. Demirkaya , “Improving SNR in PET images by using anisotropic diffusion filtration,” Engineering in Medicine and Biology Society, 2000. Proceedings of the 22nd Annual International Conference of the IEEE, vol. 1, pp. 501 -503, 2000.[4]P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Machine Intell., vol. 7, pp. 629—639, 1990.[5]I. El-Fallah and G. E. Ford, “The evolution of mean curvature in image filtering,” IEEE Int. Conf. Image Processing, vol. 1, pp. 298-302, 1994.[6]I. El-Fallah and G. E. Ford, “Mean curvature evolution and surface area scaling in image filtering,” Conf. Record of the 28th Asilomar Conf. On Signals, Systems and Computers, vol. 1, pp. 213-217, 1994.[7]I. El-Fallah and G. E. Ford, “Mean curvature evolution and surface area scaling in image filtering,” IEEE transactions on image processing, vol. 6, no. 5, pp. 750-753, 1997.[8]http://info.cipic.ucdavis.edu/mspg/siap/demos/diffusion/Dm920.html[9]http://www.mathworks.com/access/helpdesk/help/techdoc/ref/erf.shtml[10]D.W. Wilson and B.M.W. Tsui, “Noise properties of filtered-backprojection and ML-EM reconstructed emission tomographic images,” IEEE Trans. Nucl. Sci., vol. 40(4), pp.1198-1203, Aug. 1993.[11]Ching-Han Hsu, “Bayesian image reconstruction techniques for positron emission tomography : Theoy and applications,” PhD dissertation, August 1998.[12]George Kontaxakis and Ludwig G. Dtruss, “Maximum likelihood algorithms for image reconstruction in positron emission tomography,” Radionuclides for Oncology — Current Status and Future Aspects, GS Limouris, SK Shukla, HF Bender, HJ Biersack (editors), MEDITHERRA Publishers, Athens, pp. 73-106,1998.[13]Sakari Alenius, “On noise reduction in iterative image reconstruction algorithms for emission tomography: Median root prior,” Tampere, August 1999.[14]Herman GT, “Image reconstruction from projections: implementation and applications,” Topics in applied physics, vol. 32, Berlin: Springer-Verlag, 1979.[15]Herman GT, “Image reconstruction from projections: the fundamentals of computerized tomography,” New York: Acdaemic Press, 1980.[16]Ing-Tsung Hsiao, “Bayesian image reconstruction for emission and transmission tomography,” PhD dissertation, pp.44-49, May 2000.[17]S. R. Deans, “The Radon Transform and some of its applications,” John Wiley and Sons Inc., New York, 1983.[18]A. C. Kak and M. Slaney, “Principles of Computerized Tomographic Imaging,” IEEE Press, NewYork, 1988.[19]A. V. Oppenheim and R. W. Schafer, “Discrete time signal processing,” Prentice Hall, 1989.[20]H. H. Barrett and W. Swindell, “Radiological imaging: the theory of image formation, detection, and processing,” Vol. Ⅰ-Ⅱ. Academic Press, Inc., pp. 430-434, 1981.[21]Bhagwat D. Ahluwalia, “Tomographic methods in nuclear medicine: physical principles, instruments, and clinical applications,” CRC Press, pp. 10-14, 1989.[22]R. M. Lewitt, “Reconstruction Algorithms: Transform Methods,” Proc. of the IEEE, vol. 71(3), March 1983.[23]L. A. Shepp and Y. Vardi, “Maximum likelihood reconstruction in Positron Emission Tomography,” IEEE Trans. Med. Imag., vol. 1(2), pp. 113-122, 1982.[24]http://rivit.cs.byu.edu/morse/550-F95/
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