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研究生:賴師璿
研究生(外文):Lai, Shin-Hsuan
論文名稱:適用於果蠅腦中嗅覺小球的三維對位及切割研究
論文名稱(外文):3D Registration and Segmentation for Glomeruli of Drosophila Brain
指導教授:陳永昌陳永昌引用關係
指導教授(外文):Chen, Yung-Chang
學位類別:碩士
校院名稱:國立清華大學
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:2010
畢業學年度:98
語文別:英文
論文頁數:56
中文關鍵詞:嗅覺小球三維對位三維切割
外文關鍵詞:glomeruliRegistrationSegmentation
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為了要了解人腦結構以及功能上面的秘密,科學家一直致力於腦方面的研究,但是由於人腦功能太複雜,而且人腦的圖片取得不易,因此科學家便找了果蠅腦來做研究,果蠅的腦部被發現和人腦有很多類似的地方:像是記憶或學習。果蠅的腦部雖然被發現有很多和人腦相似的地方,但是其構造卻簡單許多,所以果蠅腦中的結構和神經傳導的關係成為科學家研究的一項重要課題。
為了研究果蠅的相關構造及功能,腦科學中心利用共軛焦顯微鏡與染色的方式得到腦殼的模型影像切片,配合影像的切割技術,我們可以將切割完的影像重建成三維的模型,這個模型可以幫助科學家探討果蠅腦中的結構和相關問題,科學家會將一些神經網路和組織放入這個腦模型來觀察他們的相互關係。然而如果只靠單一的果蠅腦模型來做觀察是不夠合理的,最好的做法是將多個腦模型做平均,把平均完的模型拿來當作標準的模型。
我們知道了建立平均模型的重要性,因此在這篇論文我們提出一個適用於嗅覺小球合理自動化對位的理論,先經過整體的對位,接著利用局部對位的理論來做細部的調整,最後將每個模型合理的對位在一起,然後再將所有的模型做平均,建立出一個平均的嗅覺小球模型。這個對位的方法也可以適用於影像的切割,對於一開始拿到的醫學影像,我們先利用邊緣偵測來偵測出可能的嗅覺小球邊界,偵測出來的邊界有可能不是封閉的曲線,這時候我們就用以平均模型為基準的理論去切割這些邊緣資料,最後達到嗅覺小球完整的切割。我們的理論可以適用於完整的模型或是非封閉區線的邊緣資料。

As a noted topic of life science, brain research is aimed to find out the structure and function of human brain. To simplify the problem, a fruit fly, Drosophila, is chosen for research. In Drosophila brain, it is discovered that several brain controlling genes are very similar to human’s, and so as how they function.
By using the advanced confocal microscopy technology, Drosophila brain can be scanned to get 2-D glomeruli images. These images were reconstructed into individual 3-D brain models. Biologists place neural networks and other structures into the brain model. In order to research and observation, the standard model is proposed. Rather than taking any one model as a standard, it’s better to average all individual models. The averaged brain model is a standard model for image database.
Due to the importance of standard model, the registration algorithm for creating standard model is an interesting and useful topic. An automatic registration method for glomeruli is proposed for the issue. The initial position of source model and target model are different. We use the global registration to adjust the model globally. Local registration is then to adjust the position of individual glomerulus. Point-based warping is used for point’s movement. This method can be applied to automatic segmentation. First, we find the reliable edges on the original glomeruli images. Our algorithm can be applied to the image stacks. It is not necessary that the edge is closed. Our algorithm works on the closed contour and open contour.

摘要 i
Abstract ii
Table of Contents iii
List of Figures v
List of Tables viii
Chapter 1: Introduction 1
1.1 Background 1
1.2 Motivation 3
1.3 Related Work 3
1.4 Thesis Organization 4
Chapter 2: Framework and Method 6
2.1 Framework 6
2.2 Method Overview 7
Chapter 3: 3-D Automatic Registration 11
3.1 Introduction 11
3.2 Global registration 11
3.3 Local registration 19
3.4 Summary 26
Chapter 4: Automatic segmentation 27
4.1 Introduction 27
4.2 Global alignment of the bounding box 27
4.3 Global registration and local registration 32
4.4 Further warping 35
4.5 Summary 36
Chapter 5: Experimental results 37
5.1 3-D Automatic Registration 37
5.2 Automatic Segmentation 45
5.3 Discussion 52
Chapter 6: Conclusion and Future Work 53
Reference 55

[1] S. R. Olsen and R. I. Wilson, “Cracking neural circuits in a tiny brain: new approaches for understanding the neural circuitry of drosophila,” Trends in Neurosciences, 2008.
[2] Stefan Bruckner and Veronika Solteszova and M.Eduard Groller, “BrainGazer – Visual Queries for Neurobiology Research,” IEEE Visualization, 2009.
[3] P.P. Laissue and C. Reiter, “Three-Dimensional Reconstruction of the Antennal Lobe in Drosophila melanogaster,” Journal of Comparative Neurology, 1999.
[4] Ying-Cheng Chen, “Framework for Creation the Drosophila Standard Brain from Confocal Microscopy Images,” Ph.D. thesis, National Tsing Hua University, 2007.
[5] Jie Zhang and Anand Rangarajan, “A unified feature-based registration method for multimodality images,” IEEE International Symposium on Biomedical Imaging, 2004.
[6] Nikhil Gagvani, “Parameter-Controlled Volume Thinning,” Graphical Models and Image Processing 61, pp. 149-164, 1999.
[7] Y. Zhou, A. Kaufman and A. Toga, “Three-dimensional Skeleton and Centerline Generation Based on an Approximate Minimum Distance Field,” The Visual Computer 14, pp. 303-314, 1998.
[8] Y. S. Wang and T. Y. Lee, “Curve-Skeleton Extraction Using Iterative Least Squares Optimization,” IEEE Transactions on Visualization and Computer Graphics, VOL.14, July/August, 2008.
[9] D. Reniers and Jarke J. van Wijk, “Computing Multiscale Curve and Surface Skeletons of Genus 0 Shapes Using a Global Importance Measure,” IEEE Transactions on Visualization and Computer Graphics, VOL. 14, NO. 2, March/April, 2008.
[10] Jolliffe I.T, “Principal Component Analysis, Springer-Verlag. pp. 487” New York, 1986.
[11] Richard O. Duda, Peter E. Hart, David G. Stork, “pattern classification, 2nd Ed, pp.114~116”, 2000.
[12] Fred L.Bookstein, “Principal Warps: Thin-Plate Splines and the Decomposition of Deformations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vlo. 11, No.6, pp.567-585, 1989.
[13] R. Pielot, “Warping with Optimized Weighting Factors of Displacement Vectors - A New Method to Reduce Inter-Individual Variations in Brain Imaging,” 4th IEEE Southwest Symposium on Image Analysis and Interpretation, 2000.

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