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研究生:蔡承勳
研究生(外文):Cheng-SyunCai
論文名稱:應用於生物醫學影像中膠原纖維特徵之分析
論文名稱(外文):Analysis of Collagen Fiber Features in Medical Images
指導教授:李國君李國君引用關係
指導教授(外文):Gwo Giun Lee
學位類別:碩士
校院名稱:國立成功大學
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:2015
畢業學年度:103
語文別:英文
論文頁數:132
中文關鍵詞:生物醫學影像光學虛擬活體組織切片二倍頻顯微技術影像分割紋理特徵萃取Frangi filter賈伯濾波器支持向量機大津演算法膠原纖維特徵分析
外文關鍵詞:biomedical imageoptical in vivo virtual biopsySecond Harmonic Generation (SHG)image segmentationtexture feature extractionFrangi filterGabor filterSupport Vector Machine (SVM)analysis of collagen fiber features
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生物醫學影像往往潛藏許多醫學資訊,藉由影像解讀以及特徵分析,進而幫助醫學上對生理現象與疾病能有更多的了解。對於分析生物影像特徵時,我們能透過四種不同的特徵描述包含:大小、形狀、顏色、紋理,清楚地描述所需分析之醫學特徵,進而選擇所需之影像分析工具,以獲得重要的醫學資訊。本論文提出一個電腦輔助方法,應用於剖析人類皮膚膠原纖維特徵之生物醫學影像,重要的膠原纖維特徵包含其密度、方向多樣性、粗細,利用賈伯濾波器能分析影像上紋理與大小之特徵,以定量分析膠原纖維之方向與粗細,另外透過賈伯濾波器與Frangi濾波器依據膠原纖維特性萃取膠原纖維形狀特徵,並給予支持向量機訓練出一個準確分類器,以分割膠原纖維區域,進而分析膠原纖維之密度。演算法能克服利用儀器進行特徵分析之不便,且演算法與其相關文獻進行比較,本論文能提供一個全面性膠原纖維特徵之分析, 不僅在醫學影像分析上具有相當的發展潛力,對於醫學研究也具極大的醫學價值。
The medical images normally contain abundant medical information. With image interpretations and feature analyses of medical images, physiological processes or diseases can be further understood, and this could be crucial for medical advances. Regarding feature analyses, four feature descriptions (including size, shape, color, and texture) will be analyzed to clarify desired medical features. These feature descriptions help people understand each medical feature clearly, and then the image processing tool is applied to analyze every medical feature. This thesis presents an algorithm of a computer-assisted method to dissect and quantify collagen fiber features of human skin including collagen fiber density, orientation diversity and thickness in the medical image. The Gabor filter is able to extract image texture and size information, which used for quantifying collagen fiber orientation and thickness. Moreover, the Gabor filter and the Frangi filter are utilized for extracting shape information of collagen fiber according to the properties of collagen fiber, and then the support vector machine method use shape information to obtain an accurate classification to segment collagen fiber region and further analyze collagen fiber density. The proposed algorithm is able to overcome inconveniently using the instrument for feature analysis. Comparing with other related works, the proposed algorithm provides full analyses of collagen fiber features, which has not only potential in biomedical image analyzing, but also significant value to medical research.
摘 要 i
Abstract iii
誌 謝 v
List of Tables xi
List of Figures xiii
Chapter 1 Introduction 1
1.1 Introduction 1
1.2 Motivation 2
1.3 Structure of this Thesis 5
Chapter 2 Background Information 7
2.1 Image Information 7
2.2 Physical Background of the Acquired Images 9
Chapter 3 Surveys of Related Works in the Literatures 13
3.1 Feature Extraction 13
3.1.1 Fourier Transform 13
3.1.2 Wavelet Transform 15
3.1.3 Gabor Filter 17
3.1.4 Frangi Filter 22
3.1.5 Steerable Filter 24
3.1.6 Gray-level Co-occurrence Matrix 25
3.1.7 Fractal Feature Extraction 26
3.2 Clustering and Classification 27
3.2.1 K-means Clustering 28
3.2.2 Support Vector Machine (SVM) 29
3.2.3 Artificial Neural Networks (ANN) 33
3.2.4 Bayesian Networks 34
3.3 Image Segmentation 35
3.3.1 Image Thresholding 35
3.3.2 Otsu’s Method 36
3.3.3 Region Growing 38
3.3.4 Edge Detection 39
3.3.5 Graph-theoretical Method 40
Chapter 4 Proposed Algorithms 41
4.1 Block Diagram 41
4.2 Image Preprocessing 43
4.2.1 Wiener Filter 43
4.2.2 Contrast Limit Adaptive Histogram Equalization 48
4.3 Feature Extraction of Collagen Fiber 53
4.3.1 Convolution with a Gabor Filter Bank 55
4.3.2 The Extraction of Directionality Feature and Scale Feature 70
4.3.3 Structure Analysis and Eigen Decomposition 72
4.3.4 Vessel Enhancement 75
4.4 Collagen Fiber Segmentation 79
4.4.1 Analyses of Different Features for SVM method 80
4.4.2 The Different Combinations of Feature Vector 85
4.5 Density Evaluation 87
4.6 Orientation Diversity Evaluation 90
4.7 Thickness Evaluation 92
4.8 Experimental Results 93
4.8.1 Density Evaluation 101
4.8.2 Orientation Diversity Evaluation 104
4.8.3 Thickness Evaluation 107
4.9 Comparison with Previous Works 109
Chapter 5 Conclusions and Future Works 123
5.1 Conclusions 123
5.2 Future Works 124
Acknowledgments 125
References 127

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