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研究生:郭主引
研究生(外文):Guo, Ju-Yin
論文名稱:對於果蠅腦嗅覺小球的二維影像比對及半自動邊緣偵測
論文名稱(外文):Glomeruli of the Drosophila Brain:2D Image Analogy and Semi-automatic Edge Detection
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:45
中文關鍵詞:邊緣
外文關鍵詞:edge
相關次數:
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  • 下載下載:31
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對於果蠅的大腦研究一直是個顯著的議題,其中以牠觸角內腦葉的嗅學小球功能最備受矚目。科學家想藉著建立標準的嗅覺小球模型以致力收集數據;但是在立體空間中要對不同果蠅的嗅覺小球做切割,著實是一件累人又複雜的工作。如果平面上的邊緣能半自動地被找出來、並在立體空間中堆疊起來,當作可靠的導引,一個初始模型就可以更準確地朝它們變形,而且能減低人為的負擔。

為了達成半自動偵測嗅覺小球影像邊緣的需求,我們從影像比對的概念獲得靈感,設計一種方法紀錄於此論文中。使用者可提供一組參考影像當作事前資訊,包括一張原始影像以及對應它、畫好的邊緣圖。然後一張新進影像的區塊會和一個資料庫裡的多個區塊做比對;這個資料庫是藉著剪下參考影像不同方向的區塊建立的。我們採用兩種特徵:一種是對影像做高斯函式的偏微分、另一種是經過非最大值抑制的邊緣影像。甚且,為了計算兩種特徵的權重,我們把該比對過程公式化成迴歸分析的問題、再用解線性系統的方法求出權重解。另外,為了改善比對差異產生的盲點,我們設計一個門檻來預先保留較重要的邊緣。最後,一個僅需一組參考影像和一個調整門檻的架構於此產生;能夠進行影像比對、半自動地偵測邊緣,並將它們一步步地合成出來。
Cerebral researches of Drosophila melanogaster have been prominent issues. Among them the functionalities of the glomeruli in antenna lobe of D. melanogaster draw the greatest attention. Scientists are striving to gather statistics through a constructed standard glomerular model. However, it is indeed a laborious and complicated process to perform 3D segmentation of glomeruli from various flies. If edges from 2D space are semi-automatically given and stacked up as reliable guidance in the 3D space, a source model can warp to them and be segmented with higher accuracy and less manual effort.

To achieve the desire of semi-automatic edge detection on glomerular images, we developed a method inspired from the concept of ‘image analogy’ in this thesis. Users could provide a source pair, including one original image and the other the corresponding edge map, as the prior guidance. Patch-wise analogy was then applied between patches from the input image and ones from the database, which was constructed by cutting the source pair to patches with different orientations. Two kinds of feature related to gradient were adopted: one was Gaussian partial derivative, and the other was the non-maximal-suppressed edge map. Furthermore, on purpose of calculating the best weights of each feature, we formulated the analogizing process to a regression problem and solved the weights by least squares approach. We also set a threshold to keep the important edges in advance as well as improve the blind spot of the analogized error. Finally, a framework that only asked for single source pair and an adjusted threshold was formed. It performed image analogy and edge detection semi-automatically, and synthesize reliable edge maps step by step.
Abstract i
Table of Contents ii
List of Figures and Tables iii

Chapter 1 : Introduction 1
1.1 About Glomeruli 1
1.2 Motivation 3
1.3 Related Work 4
1.4 Thesis Organization 6
Chapter 2 : System Overview and Method Framework 7
2.1 System Overview 7
2.2 Method Framework 8
Chapter 3 : Image Characteristics and Preprocessing 11
3.1 Image Characteristics 11
3.2 ROI Extraction 12
3.3 Feature Computation 16
Chapter 4 : Analogy, Synthesis and Threshold 22
4.1 Structure 22
4.2 Multiple Feature Weights 24
4.3 Threshold 33
Chapter 5 : Result and Discussion 37
Chapter 6 : Conclusion and Future Work 42
References 44
[1] Gregory SXE Jefferis, Elizabeth C Marin, Ryan J Watts, and Liqun Luo, “Development of neuronal connectivity in Drosophila antennal lobes and mushroom bodies”, Current Opinion in Neurobiology, 12, 80-86, 2002.
[2] Africa Couto, Mattias Alenius, and Barry J. Dickson, “Molecular, Anatomical, and Functional Organization of the Drosopjila Olfactory System”, Current Biology, vol.15, 1535-1547, 2005.
[3] P.P.Laissue, C.Reiter, P.R.Hiesinger, S.Halter, K.F.Fischbach, and R.F.Stocker, “Three-Dimensional Reconstruction of the Antennal Lobe in Drosophila melanogaster”, The Journal of Comparative Neurology, 405, 543-552, 1999.
[4] Pinaki Sarder and Arye Nehorai, “Deconvolution Methods for 3-D Fluorescence Microscopy Images”, IEEE Signal Processing Magazine, 32, 2006.
[5] A.Garrido and N.Perez de la Blanca, “Applying deformable templates for cell image segmentation”, Pattern Recognition, 33, 821-832, 2000.
[6] Pingkun Yan and Ashraf A.Kassim, “Medical Image Segmentation Using Minimal Path Deformable Models With Implicit Shape Priors”, IEEE transactions on Information Technology in Biomedicine, vol.10, no.4, 2006.
[7] Jun Zhang and Jiulun Fan, “Medical Image Segmentation Based on Wavelet Transform and Watershed Algorithm”, IEEE International Conference on Information Ascquisition, 2006.
[8] Jun Zhang and Jiulun Fan, “Glomerulus Extraction Based on Genetic Algorithm and Watershed Transform”, IEEE International Conference on Intelligent Robots and Systems, 2006.
[9] Jiaxin Ma, Jun Zhang, and Jinglu Hu, “Glomerulus Extraction by Using Genetic Algorithm for Edge Patching”, Digital Object Identifier, 2009.
[10] Alexei A.Efros and Thomas K.Leung, “Texture Synthesis by Non-parametric Sampling”, IEEE International Conference on Computer Vision, 1999.
[11] Li-Yi Wei and Marc Levoy, “Fast Texture Synthesis using Tree-structured Vector Quantization”, SIGGRAPH 2000, 479-488, 2000.
[12] Michael Ashikhmin, “Synthesizing Natural Textures”, 2001 ACM Symposium on Interactive 3D Graphics, 217-226, 2001.
[13] Aaron Hertzmann, Charles E.Jacobs, Nuria Oliver, Brian Curless, and David H.Salesin, “Image Analogies”, SIGGRAPH 2001, 2001.
[14] Sameer Agarwal and Serge Belongie, “Segmentation by Example”, 2002.
[15] William T.Freeman, Joshua B.Tenenbaum, and Egon C.Pasztor, “Learning Style Translation for the Lines of a Drawing”, ACM Transactions on Graphics, vol.22, 33-46, 2003.
[16] Tiberio S.Caetano, Julian J.McAuley, Li Cheng, Quoc V.Le, and Alex J.Smola, “Learning Graph Matching”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, no.6, 2009.
[17] Christopher M.Bishop, “Pattern Recognition and Machine Learning”, p.137~p.147.
[18] Nuno Vieira Lopes, Pedro A.Mogadouro do Cuoto, and Humberto Bustince, “Automatic Histogram Threshold Using Fuzzy Measures”, IEEE Transactions on Image Processing, vol.19, no.1, 2010.
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