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研究生： 林煥翔
Lin, Huan-Hsiang
論文名稱： 應用於生物醫學影像及三維視訊處理之影像及影像序列分析
Image and Image Sequence Analysis with Applications to Biomedical Imaging and 3-D Video Processing
指導教授： 李國君
Lee, Gwo-Giun
學位類別： 碩士
Master
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： 2012
畢業學年度： 100
語文別： 英文
論文頁數： 196
中文關鍵詞： 生物醫學影像 、光學虛擬活體組織切片 、三倍頻顯微技術 、細胞分割 、細胞核質比 、分水嶺分割演算法 、區域特徵提取 、收斂指數濾波器 、三維視訊處理 、二維視訊轉三維視訊技術 、影像分割 、紋理特徵萃取 、羅斯遮罩 、譜聚類 、拉普拉斯矩陣
外文關鍵詞： biomedical imaging, optical in vivo virtual biopsy, Third Harmonic Generation (THG), cell segmentation, Nuclear-to-Cytoplasmic ratio (NC ratio), watershed transformation, blob detection, convergence index filter, 3-D video processing, 2-D to 3-D video conversion, image segmentation, texture feature extraction, spectral clustering, graph Laplacian matrix
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    • 在真實世界當中，我們可以藉由下列幾個步驟來充分地瞭解自然界的一些現象，這些步驟包含了：擷取、處理、分析、解讀。本論文針對影像及影像序列之處理與分析提出三個演算法並分別應用於生物醫學影像及三維視訊處理。針對生物醫學影像的應用，本論文提出一個自動化電腦輔助設計之細胞分割及細胞核質比分析的演算法。由實驗結果以及與相關文獻之比較，本論文所提出之演算法不僅在生物醫學影像分析之領域具有極高的發展潛力，也於很多應用當中具備極大之醫學價值。此外，針對三維視訊處理之應用，本論文提出一個低複雜度之紋理特徵萃取器以及一個具有容易實現且能有效率地求解的特性之聚類演算法，並且應用於二維視訊轉三維視訊之技術。藉由與其他紋理特徵萃取器與分/聚類演算法之複雜度分析以及在主觀和客觀方面的效能估測，在多媒體應用當中，本論文所提出之聚類演算法以及低複雜度紋理特徵萃取器具有優異的效能表現以及容易實現於即時系統中硬體架構之優點。

    In the real world, four procedures including acquisition, processing, analysis, and interpretation are required to understand the real-life phenomena comprehensively. In this thesis, three algorithms concentrating on the processing and analysis procedures applied to images and image sequences with applications to biomedical imaging and 3-D video processing are proposed. For the application of biomedical imaging, an automatic computer-aided design for cell segmentation and NC ratio analysis is developed and has significant potential for biomedical imaging analysis and medical values in a variety of applications according to experimental results and compatible performance as compared to related works. For the another application to 3-D video processing, a texture feature extractor with low complexity and a clustering algorithm which is simply implemented and can be efficiently resolved are proposed with application to 2-D to 3-D video conversion and compared with related works by complexity analysis and performance evaluation in both objective measurement and subjective viewing to make sure that it has compatible performance and hardware-friendly implementation for the real-time systems in the application of multimedia.

    摘 要 i Abstract iii 誌 謝 v Table of Contents vii List of Tables xiii List of Figures xv Chapter 1 Introduction 1 1.1 Introduction 1 1.2 Motivation 2 1.2.1 Biomedical Imaging 2 1.2.2 3-D Video Processing 3 1.3 Organization of this Thesis 5 Chapter 2 Background Information 7 2.1 Biomedical Imaging 7 2.1.1 Conventional Biopsy Methodology 7 2.1.2 Physical Background of the Acquired Images 7 2.2 3-D Video Processing 11 2.2.1 Depth Estimation 11 2.2.2 Stereoscopic View Synthesis 12 2.2.3 3-D Video Rendering 12 Chapter 3 Surveys of Related Works in the Literatures 13 3.1 Cell Segmentation 13 3.1.1 Image Thresholding 13 3.1.2 Watershed Transformation 14 3.1.2.1 Fragment Merging Strategy 17 3.1.2.2 Marker-Controlled Strategy 17 3.1.3 Spectral Clustering 17 3.1.4 Deformable Models 18 3.1.5 Convergence Index (CI) Family 18 3.1.5.1 Convergence Index Filter (CF) 19 3.1.5.2 Simplified Version of Convergence Index Filter 21 3.1.5.3 Iris Filter (IF) 23 3.1.5.4 Adaptive Ring Filter (ARF) 26 3.1.5.5 Sliding Band Filter (SBF) 28 3.2 Feature Extraction and Dimensionality Reduction 30 3.2.1 Fourier Analysis 31 3.2.2 Discrete Cosine Transform (DCT) 33 3.2.3 Gabor Filter 35 3.2.4 Wavelet Transform 35 3.2.5 Laws’ Filter 39 3.2.5.1 1-D Laws’ Vectors 39 3.2.5.2 2-D Laws’ Masks 42 3.2.6 Principal Component Analysis (PCA) 52 3.2.7 Independent Component Analysis (ICA) 53 3.2.8 Linear Discriminant Analysis (LDA) 54 3.3 Classification and Clustering 55 3.3.1 Support Vector Machine (SVM) 56 3.3.2 K-means Clustering 59 3.3.3 K-medoids Clustering 62 3.3.4 Spectral Clustering 62 Chapter 4 Proposed Algorithms 65 4.1 Cell Segmentation and NC Ratio Analysis in Biomedical Imaging 65 4.1.1 Block Diagram 65 4.1.2 Nuclei Segmentation 68 4.1.2.1 Watershed Transformation with Marker-Controlled Strategy 68 4.1.2.2 Nuclei Initialization 69 4.1.2.2.1 Blob Detection 70 4.1.2.2.2 Outlier Removal 76 4.1.2.2.3 Distance Transform 76 4.1.2.3 Morphological Image Processing 79 4.1.2.4 Nuclei Detection and Validation 85 4.1.3 Cytoplasm Segmentation 87 4.1.3.1 Cytoplasm Initialization 90 4.1.3.2 Cytoplasm Detection 92 4.1.4 Cell Size and NC Ratio Evaluation 96 4.1.5 Experimental Results 96 4.1.6 Comparisons 106 4.2 Texture Feature Extraction in 3-D Video Processing 107 4.2.1 Texture Feature Extraction 107 4.2.1.1 Block Diagram 108 4.2.1.2 Filtering Procedure 112 4.2.1.3 Local Energy Estimation 112 4.2.2 Texture Descriptors 113 4.2.3 Texture Measurement 114 4.2.3.1 Gabor Filters 114 4.2.3.2 Laws’ Masks 119 4.2.3.3 Modified Laws’ Masks 125 4.2.4 Comparison of the Filter Responses 128 4.2.5 Performance Evaluation 131 4.2.6 Complexity Analysis 138 4.2.6.1 Non-intrinsic Complexity Measure 139 4.2.6.1.1 Processing Time 139 4.2.6.2 Intrinsic Complexity Measure 140 4.2.6.2.1 Number of Operations 141 4.2.6.2.2 Data Transfer 147 4.2.7 Experimental Results and Comparisons 156 4.3 Clustering with Color Features in 3-D Video Processing 164 4.3.1 Block Diagram 164 4.3.2 Graph Partitioning Problem 165 4.3.3 Eigenvalue System 170 4.3.3.1 Graph Laplacian Matrix 171 4.3.3.2 Physical Meaning 174 4.3.3.3 Eigenvalue Decomposition 177 4.3.4 Complexity Analysis 178 4.3.5 Experimental Results and Comparisons 179 Chapter 5 Conclusions and Future Works 185 5.1 Conclusions 185 5.2 Future Works 186 Acknowledgments 189 References 191

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