||Analysis of Collagen Fiber Features in Medical Images
||Department of Electrical Engineering
optical in vivo virtual biopsy
Second Harmonic Generation (SHG)
texture feature extraction
Support Vector Machine (SVM)
analysis of collagen fiber features
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
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
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