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系統識別號 U0026-0812200910443290
論文名稱(中文) 腦部功能影像之三維對位與分析
論文名稱(英文) 3D Registration and Analysis for Brain Functional Images
校院名稱 成功大學
系所名稱(中) 資訊工程學系碩博士班
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 91
學期 2
出版年 92
研究生(中文) 廖元麟
研究生(英文) Yuan-Lin Liao
電子信箱 liaoyuanlin@yahoo.com.tw
學號 p7690409
學位類別 碩士
語文別 英文
論文頁數 72頁
口試委員 口試委員-劉興民
口試委員-柯建全
口試委員-陳永昌
口試委員-陳天送
指導教授-孫永年
口試委員-鄭國順
口試委員-謝孟達
中文關鍵字 腦功能影像  共同資訊量  影像對位 
英文關鍵字 mutual information  brain functional images  image registration 
學科別分類
中文摘要 Tc-99m HMPAO是一種用於腦血流分析的典型顯跡物(tracer),利用Tc-99m HMPAO的單光子電腦斷層掃描(SPECT)腦部攝影便成為一項評估腦功能時相當常用的技術。
在本論文中,我們提出一套針對SPECT影像的分析系統,執行一系列的影像處理步驟,包含三維影像對位、腦部區域萃取、灰階值正規化,使所有三維腦部資料都對應到相同空間上,以便進行後續分析。接下來就將對齊的影像來作一項標準的統計分析—配對t檢定,來偵測出兩組影像中具有顯著差異的區域。最後我們就將所得之統計t圖譜一張張用彩色圖表示,指出活化病灶區所在。此結果圖對於醫師針對精神分裂症患者的病灶評估和定位是相當有幫助的。
除此之外,我們特別對藉由共同資訊量(mutual information, MI)最大化之影像對位作研究。首先描述用來導出機率分佈函數及算出MI值的共同統計圖(joint histogram)。接著再針對由部分體積(partial volume, PV)內插法導致的內插假象(interpolation artifact)作討論和回顧。然後我們提出以權重方式(weighting method)將區域共同機率(local joint probability)加入原有共同機率計算MI值的方法有效解決假象問題。在計算成本的考量之下,我們也同時建立一個多解析度架構以減少搜尋時間。
對位結果利用由實際影像設計之假體影像以及所得之臨床SPECT影像來作評估。我們證實此方法可達到次體素正確性(subvoxel accuracy),並維持前後一致性(consistency),同時所提之權重方法也優於傳統PV內插法。
英文摘要 Tc-99m HMPAO is a typical tracer used in the analysis of cerebral blood flow. Single photon emission computed tomography (SPECT) brain imaging utilizing Tc-99m HMPAO is thus a popular method to assess brain function.

In this thesis, we present an image analysis system for SPECT images that performs a series of image processing procedures including 3D image registration, brain extraction, and gray-level normalization, which map all the 3D brain data to the same space for further analysis. Afterward, the aligned images undertake a standard statistical analysis, the paired t test, to detect the areas that have significant deviations between the two images. The statistical t map is then represented with a color plot for each brain slice to indicate the activation foci. The resulting maps are found very helpful to doctors for the lesion evaluation and localization in the clinical diagnosis of schizophrenic patients.

Besides, a study on image registration by maximization of mutual information is also given. We address the concept of joint histogram, which is used to derive the probability distribution and thus compute the mutual information (MI) value. A well-known interpolation artifact problem caused from the partial volume (PV) interpolation is discussed and reviewed here. Hence, we propose a weighting method, which adds the local joint probability term to the original joint probability, to eliminate the artifacts effectively. Under the consideration to computational cost, we also construct a multiresolution hierarchy to reduce the search time.

The registration results are evaluated using the designed phantom and the acquired clinical SPECT images. We show that the subvoxel accuracy is achieved and the consistency is also maintained. It is also proved to be superior to the PV interpolation when the proposed weighting method is used.
論文目次 Chapter 1 Introduction .................................................................... 1
1.1 Motivation ............................................................................ 1
1.2 Outlines .............................................................................. 3
Chapter 2 Medical Image Registration .......................................................4
2.1 Concepts ...............................................................................4
2.2 Related Issues of Registration .........................................................8
2.3 Review .................................................................................9
Chapter 3 Image Processing and Analysis Procedures ........................................ 14
3.1 Data Acquisition ...................................................................... 14
3.2 Image Registration .................................................................... 15
3.3 Brain Extraction ...................................................................... 17
3.4 Intensity Normalization ............................................................... 19
3.5 Statistical Analysis .................................................................. 20
Chapter 4 Mutual Information .............................................................. 22
4.1 Theory ................................................................................ 22
4.2 Joint Histogram ....................................................................... 25
4.3 Interpolation ......................................................................... 28
4.4 Optimization .......................................................................... 45
4.5 Multiresolution ....................................................................... 47
Chapter 5 Validation and Discussion ....................................................... 49
5.1 Designed Phantom Registration ......................................................... 49
5.2 Clinical Data Registration ............................................................ 54
5.3 Visual Inspection ..................................................................... 59

Chapter 6 Conclusion and Future Work ...................................................... 63
6.1 Conclusion ............................................................................ 63
6.2 Future Researches ..................................................................... 64
Appendix .................................................................................. 66
References ................................................................................ 67
Vita ...................................................................................... 72
參考文獻 [1] J.V. Hajnal, D.L.G. Hill, and D.J. Hawkes, Medical Image Registration, CRC Press, 2001.

[2] J.B.A. Maintz and M.A. Viergever, “A survey of medical image registration”, Med. Image Anal., vol. 2, iss. 1, pp. 1-36, 1998.

[3] D.L.G. Hill, P.G. Batchelor, M. Holden, and D.J. Hawkes, “Medical image registration”, Phys. Med. Bol., vol. 46, no. 3, pp. R1-R45, 2001.

[4] B.F. Hutton, M. Braun, L. Thurfjell, and D.Y.H. Lau, “Image registration: an essential tool for nuclear medicine”, Eur. J. Nucl. Med. Mol. Imaging, vol. 29, no. 4, pp. 559-577, 2002.

[5] J.V. Hajnal, N. Saeed, E.J. Soar, A. Oatridge, I.R. Young, and G.M. Bydder, “A registration and interpolation procedure for subvoxel matching of serially acquired MR images”, J. Comput. Assist. Tomogr., vol. 19, iss. 5, pp. 289-296, 1995.

[6] K.J. Friston, J. Ashburner, J.B. Poline, C.D. Frith, J.D. Heather, and R.S.J. Frackowiak, “Spatial registration and normalization of images”, Human Brain Mapping, vol. 2, iss. 3, pp. 165-189, 1995.

[7] J. Ashburner and K.J. Friston, “Nonlinear spatial normalization using basis functions”, Human Brain Mapping, vol. 7, iss. 4, pp. 254-266, 1999.

[8] A. Roche, G. Malandain, X. Pennec, and N. Ayache, “The correlation ratio as a new similarity measure for multimodal image registration”, in Proc. of First Int. Conf. on Medical Image Computing and Computer-Assisted Intervention (MICCAI'98), vol. 1496 of Lecture Notes in Computer Science, Springer Verlag, Cambridge, U.K., pp. 1115-1124, 1998.

[9] R.P. Woods, J.C. Mazziotta, and S.R. Cherry, “Rapid automated algorithm for aligning and reslicing PET images”, J. Comput. Assist. Tomogr., vol. 16, iss. 4, pp. 620-633, 1992.

[10] R.P. Woods, J.C. Mazziotta, and S.R. Cherry, “MRI-PET registration with automated algorithm”, J. Comput. Assist. Tomogr., vol. 17, iss. 4, pp. 536-546, 1993.

[11] C.E. Shannon, “The mathematical theory of communication (parts 1 and 2)”, Bell Syst. Tech. J., vol. 27, pp. 379-423 and pp. 623-656, 1948.

[12] A. Collignon, F. Maes, D. Delaere, D. Vandermeulen, P. Suetens, and G. Marchal, “Automated multi-modality image registration based on information theory”, in Information Process. Med. Imaging 1995, Y. Bizais, C. Barillot, and R Di Paola, Eds., Kluwer Academic, Dordrecht, The Netherlands, pp. 263-274, 1995.

[13] F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, “Multimodality image registration by maximization of mutual information”, IEEE Trans. Med. Imag., vol. 16, no. 2, pp. 187-198, 1997.

[14] W.M. Wells III, P. Viola, H. Atsumi, S. Nakajima, and R. Kikinis, “Multi-modal volume registration by maximization of mutual information”, Med. Image. Anal., vol. 1, iss. 1, pp. 35-51, 1996.

[15] P.A. Viola, Alignment by Maximization of Mutual Information, PhD thesis, Massachusetts Institute of Technology, 1995.

[16] C. Studholme, D.L.G. Hill, and D.J. Hawkes, “An overlap invariant entropy measure of 3D medical image alignment”, Pattern Recognit., vol. 32, iss. 1, pp. 71-86, 1999.

[17] O. Monchi, M. Petrides, V. Petre, K. Worsley, and A. Dagher, “Wisconsin card sorting revisited: distinct neural circuits participating in different stages of the task identified by event-related functional magnetic resonance imaging”, J. Neurosci., vol. 21, no. 19, pp. 7733-7741, 2001.

[18] P. Thevenaz, U.E. Ruttimann, and M. Unser, “A pyramid approach to subpixel registration based on intensity”, IEEE Trans. Image Processing, vol. 7, no. 1, pp. 27-41, 1998.

[19] N.T. Chiu, T.Y. Chien, Y.L. Liao, W.Y Sheu, and Y.N. Sun, “Intra-modality registration for assessing brain function from SPECT volume images”, in Computer Graphics Workshop, Tainan, Taiwan, 2002.
[20] W. H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling, Numerical Recipes in C, 2nd ed., Cambridge University Press, Cambridge, U.K., 1992.

[21] M. Sonka, V. Hlavac, and R. Boyle, Image Processing Analysis, and Machine Vision, 2nd ed., PWS Publishing, 1999.

[22] C.M. Gullion, M.D. Devous, Sr., and A.J. Rush, “Effects of four normalizing methods on data analytic results in functional brain imaging”, Biol. Psychiatry, vol. 40, no. 11, pp. 1106-1121, 1996.

[23] R.G. Marco, E.J.A. Garica-Iturrospe, L.F. Lopez, M.R.C. Mendez, O.H. Rodriguez, A.D. Ramirez, J.H. Martinez, and M.S. Keshavan, “Hypofrontality in schizophrenia: influence of normalization methods”, Prog. Neuro-Psychopharmacol & Biol. Psychiatry, vol. 21, no. 8, pp. 1239-1256, 1997.

[24] C. Pérault, D. Papathanassiou, H. Wampach, P. Véra, A. Kaminska, C. Chiron, P. Peruzzi, and J-C, Liehn, “Computer-aided intrapatient comparison of brain SPECT images: the gray-level normalization issue applied to children with epilepsy”, J. Nucl. Med., vol. 43, no. 6, pp. 715-724, 2002.

[25] Y.N. Sun, S.C. Huang, N.T. Chiu, C.Y. Yu, and F.J. Chen, “Bullseye display of cerebral cortical blood flow”, IEEE Eng. in Med. and Biol., vol. 21, iss. 4, pp. 79-85, 2002.

[26] T.M. Cover and J.A. Thomas, Elements of Information Theory, John Wiley & Sons, New York, 1991.

[27] I. Vajda, Theory of Statistical Inference and Information, Kluwer Academic, Dordrecht, The Netherlands, 1989.

[28] R. Duda and P. Hart, Pattern Classification and Scene Analysis, John Wiley and Sons, 1973.

[29] J. Tsao and P. Lauterbur, “Generalized clustering-based registration for multi-modality images”, in Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 20, no. 2, pp. 667-670, 1998.

[30] D.L.G. Hill, D.J. Hawkes, N. A. Harrison, and C.F. Ruff, “A strategy for automated multimodality image registration incorporating anatomical knowledge and imager characteristics,” in Information Processing in Medical Imaging, H.H. Barrett and A.F. Gmitro, Eds., vol. 687 of Lecture Notes in Computer Science, pp. 182–196, 1993.

[31] J.P.W. Pluim, J.B.A. Maintz, and M.A. Viergever, “Interpolation artefacts in mutual information-based image registration”, Computer Vision and Image Understanding, vol. 77, iss. 2, pp. 211-232, 2000.

[32] D. Sarrut and F. Feschet. “The partial intensity difference interpolation”, in International Conference on Imaging Science, Systems and Technology, H. R. Arabnia, Eds, CSREA Press, Las Vegas, USA, pp. 46-51, 1999.

[33] H.M. Chen and P.K, Varshney, “Registration of multimodal brain images: some experimental results”, in Proc. SPIE Conference on Sensor Fusion: Architectures, Algorithms, and Applications VI, vol. 4731, B.V. Dasarathr, Eds, pp.122-133, 2002.

[34] B. Likar and F. Pernuš, “A hierarchical approach to elastic registration based on mutual information”, Image and Vision Computing, vol. 19, iss. 1-2, pp. 33-44, 2001.

[35] J. Tsao, “Efficient interpolation for clustering-based multimodality image registration”, in Proceedings of the Seventh Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM), Philadelphia, PA, May 22-28, vol. 3, p. 2195, 1999.

[36] F. Maes, D. Vandermeulen, and P. Suetens, “Comparative evaluation of multi- resolution optimization strategies for multimodality image registration by maximization of mutual information”, Med. Image. Anal., vol. 3, iss. 4, pp. 373-386, 1999.

[37] P. Thévenaz and M. Unser, “Optimization of mutual information for multiresolution image registration”, IEEE Trans. Image Processing, vol. 9, no. 12, pp. 2083-2099, 2000.

[38] M. Jenkinson and S. Smith, “A global optimisation method for robust affine registration of brain images”, Med. Image. Anal., vol. 5, iss. 2, pp. 143-156, 2001.

[39] M. Jenkinson, P. Bannister, M. Brady, and S. Smith, “Improved optimization for the robust and accurate linear registration and motion correction of brain images”, NeuroImage, vol. 17, iss. 2, pp. 825-841, 2002.

[40] J.P.W. Pluim, J.B.A. Maintz, and M.A. Viergever, “Mutual information matching in multiresolution contexts”, Image and Vision Computing, vol. 19, iss. 1-2, pp. 45-52, 2001.

[41] H.M. Chen and P.K, Varshney, “A pyramid approach for multimodality image registration based on mutual information” in Proceedings of 3rd international conference on information fusion, vol. I, pp. 9-15, 2000.

[42] T.Y. Lee, T.L. Weng, and Y.N. Sun, “Optimized semi-boundary (SB) rendering scheme”, J. Inf. Sci. Eng., vol. 15, no. 6, pp. 845-858, 1999.

[43] J. Huang and D. Mumford, “Statistics of natural images and models”, in Proc. Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 1541-1547, 1999.
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