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研究生： 賴永進
Lay, Young-Jinn
論文名稱： “群組行進立方體”(GMC)演算法及其應用
“Group Marching Cube” (GMC) Algorithm and Its Applications
指導教授： 陳立祥
Chen, Lih-Shyang
林啟禎
Lin, Chii-Jeng
學位類別： 博士
Doctor
系所名稱： 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2011
畢業學年度： 99
語文別： 英文
論文頁數： 95
中文關鍵詞： 行進立方體演算法 、表面偵測 、醫學教育 、e化學習 、光學微影 、三維積體電路
外文關鍵詞： Marching cube algorithm, iso-surface detection, medical education, e-Learning, Lithography, 3D IC
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    • 立體醫學影像表面的產生，是指由一系列二維的影像重建產生三維物件，而重建後的立體醫學影像，其影像品質會因三維重建技術的不同而有所差異。本論文包含三個相關領域 (1) 群組行進立方體重建演算法，此演算法應用到下列兩個系統， (2) 2D及3D的醫學解剖學習系統，及 (3) 積體電路製程之光學微影3D模擬系統。
    群組行進立方體（Group Marching Cube）重建演算法利用分享邊的機制，將原始行進立方體演算法增加速度，對於互動式物件的操作特別有用。實驗結果對於增加的速度最多可以達到22倍。
    對於解剖學習系統而言，人體解剖學是讓醫學院的學生學習人體器官的形狀、位置、大小、功能、及與其他器官空間上的關係的一門學問，讓醫學院的學生可以很容易的、很有效率的學習人體解剖學的2D及3D的學習系統。同時，系統並利用此資料中的CT及MRI的影像，配合原有的彩色醫學影像，讓學生可以學習如何解讀他們將來在臨床上會遇到的CT及MRI醫學影像。
    目前IC製程核心技術為光學微影，因為光的某些特性往往使得製造結果無法達到佈局工程師的預期結果，在實際製程前我們會先進行模擬，本研究利用Terrain Mesh Rendering技術來呈現模擬結果，利用表格法來偵測模擬與預期結果間的誤差。

    Surface generation of 3D medical images uses a series of 2D images to rebuild the 3D objects. The quality of rebuilt 3D medical objects will be different based upon the 3D reconstruction methods. This dissertation includes three related parts: (1) the “Group Marching Cube” reconstruction algorithm that is used in the following two systems (2) a 2D and 3D medical anatomy learning system and (3) a 3D simulation system that simulates the lithography of integrated circuit (IC) manufacturing process.
    The “Group Marching Cube” reconstruction algorithm speeds up the original Marching Cube algorithm and can be particularly useful for interactive object manipulation. The experimental result can achieve at most 22 times faster.
    As to the anatomy learning system, human anatomy is the basic scientific study for students in medical schools to learn the shape, position, size, and various relationships of the organ structures in the human body. The 2D and 3D anatomy learning system can be used by the students in medical schools to learn the anatomy more effective and efficient. The system also uses some associated magnetic resonance imaging (MRI) and computed tomography (CT) images for the students to learn how to interpret the MRI and CT images that the students will face in their future medical practices.
    For the lithography simulation system, because of the light characteristics in the lithography of the IC manufacturing process can’t achieve results which engineers anticipate, there is a need to simulate and estimate what the lithography results are going to be before they are produced. The lithography simulation system uses a table method to detect errors between the anticipated and practical results and makes use of a terrain mesh rendering to show simulation result.

    摘要 IV ABSTRACT V 誌謝 VII CHAPTER 1. INTRODUCTION 1 CHAPTER 2. RELATED BACKGROUNDS 5 2.1 3D reconstructions 5 2.1.1 The slow surface extraction process of the MC algorithm 5 2.1.2 The problems with a hash table 6 2.1.3 The effects of hash table on the overall performance of the MC algorithm 7 2.1.4 Discussion of the timing performance of the MC algorithms 12 2.2 Medical images for anatomy education 14 2.2.1 The difficulty of learning anatomy 14 2.2.2 Anatomical cross-sectional image datasets 15 2.2.3 e-Learning for anatomical education 16 2.3 The simulation of the lithographic process 17 2.4 TSV/3DIC profile metrology 19 CHAPTER 3. A “GROUP MARCHING CUBE” (GMC) ALGORITHM FOR SPEEDING UP THE MARCHING CUBE ALGORITHM 23 3.1 Introduction 23 3.1.1 Voxel-based object representation 23 3.1.2 Needs for the GMC algorithm 23 3.2 The surface extraction algorithm 27 3.2.1 Pre-processing 27 3.2.2 Introduction to the concept of the GMC 28 3.2.3 A simple 2D example 32 3.3 Discussions of the related issues 40 3.3.1 Discussion of the additional memory needed by the GMC algorithm 40 3.3.2 How to combine the GMC algorithm with the other existing MC acceleration algorithms 41 3.3.3 How to parallelize the GMC algorithm 43 3.4 Summary 43 CHAPTER 4. 2D AND 3D MEDICAL IMAGES FOR ANATOMY EDUCATION 45 4.1 Introduction 45 4.2 System architecture 46 4.2.1 2D object segmentation 47 4.2.2 Assignment of organ names 49 4.2.3 3D object reconstruction 50 4.2.4 Correction of the 2D organ segmentation and assignments of organ names 50 4.2.5 The establishment of the anatomy hierarchical structure 52 4.2.6 The establishment of organ database 53 4.3 System functions for anatomy education 54 4.3.1 2D and 3D display 54 4.3.2 Learning CT and MRI images 56 4.3.3 Manipulation of the 3D object 61 4.4 Current status of the system 64 4.5 Summary 64 CHAPTER 5. THE 3D DISPLAY AND ANALYSIS OF THE PATTERN OF PHOTOLITHOGRAPHY AND TSV/3DIC PROFILE METROLOGY 65 5.1 Introduction 65 5.2 The data processing procedures of the 3D Builder 66 5.3 Defect analysis 71 5.4 TSV images pre-processing 80 5.5 TSV 3D profile reconstruction 83 5.6 Summary 86 CHAPTER 6. CONCLUSIONS 88 REFERENCES 90

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