共聚焦显微镜图像的图像配准和图象拼接博士论文

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共聚焦显微镜图像的图像配准和图象拼接博士论文。本文提出了一种创新的图像配准方法解决了共聚焦显微镜的硬件限制。另外,文章还提供了宽视场FOV光学检测完成了更大图像的图像拼接。

Image Registration and Mosaicing for Dynamic In Vivo Fibered Confocal Microscopy

 Image Registration and Mosaicing for Dynamic In Vivo Fibered Confocal Microscopy. The main goal of this thesis is to move beyond current hardware limitations of fibered confocal microscopy (FCM) by developing specific innovative image registration schemes. In particular, we provide wide field-of-view (FOV) optical biopsies to the clinicians through fully automatic stitching of images within large mosaics.

Optimization Methods for Linear Image Registration

Contents

3.1 Motivation: Fast and Robust Alignment of Pairs of Images 31

3.2 Rigorous Mathematical Framework For Image Registration 33

3.2.1 Image Registration Model . . . . . . . . . . . . . . . . . . . . 33

3.2.2 Newton Methods for Lie Groups . . . . . . . . . . . . . . . . 33

3.2.3 Gauss-Newton for Image Registration . . . . . . . . . . . . . 35

3.3 Efficient Second-Order Minimization (ESM) . . . . . . . . . 37

3.3.1 A Second-Order Linearization . . . . . . . . . . . . . . . . . . 37

3.3.2 Example: 2D Rigid Body Transformations . . . . . . . . . . . 39

3.4 Region Tracking Algorithms for Cell Traffic Analysis . . . . 42

3.4.1 Region-of-Interest Tracker . . . . . . . . . . . . . . . . . . . . 42

3.4.2 Application to Cell Trafficking . . . . . . . . . . . . . . . . . 44

3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

 

Efficient Diffeomorphic Image Registration

 

Table of Contents

4.1 Motivation: Fast Compensation of Tissue Deformation . . . 51

4.2 An Insight into the Demons Algorithm . . . . . . . . . . . . 53

4.2.1 A Deeper Understanding of the Alternate Optimization . . . 54

4.2.2 Compositive and Additive Demons . . . . . . . . . . . . . . . 55

4.2.3 Demons Forces . . . . . . . . . . . . . . . . . . . . . . . . . . 56

4.2.4 Linearization of the Intensity Difference . . . . . . . . . . . . 57

4.3 Experiments: Practical Advantage of the Symmetric Forces 61

4.4 Introducing Diffeomorphisms into the Demons . . . . . . . . 64

4.4.1 A Lie Group Structure on Diffeomorphisms . . . . . . . . . . 65

4.4.2 Diffeomorphic Demons Algorithm . . . . . . . . . . . . . . . . 66

4.4.3 Linearization of the Intensity Difference . . . . . . . . . . . . 67

4.5 Experiments: Diffeomorphic Registration Can Be Fast . . . 68

4.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

 

 

Robust Mosaicing for Fibered Confocal Microscopy

Table of Contents

5.1 Motivation: Algorithmically Improving FOV & Resolution 78

5.2 Problem Statement and Overview of the Algorithm . . . . 80

5.2.1 Observation Model . . . . . . . . . . . . . . . . . . . . . . . . 81

5.2.2 Overview of the Algorithm . . . . . . . . . . . . . . . . . . . 82

5.3 Basic Tools for Estimation Problems on Lie Groups . . . . 83

5.3.1 Left Invariant Metric and Distance . . . . . . . . . . . . . . . 84

5.3.2 Riemannian Exponential and Logarithm Maps . . . . . . . . 85

5.3.3 Mean and Covariance Matrix . . . . . . . . . . . . . . . . . . 86

5.4 From Local to Global Alignment . . . . . . . . . . . . . . . . 87

5.4.1 Framework for Global Positioning . . . . . . . . . . . . . . . 88

5.4.2 A Lie Group Approach for Global Positioning . . . . . . . . . 89

5.4.3 Riemannian Method for Non-linear Least Squares . . . . . . 89

5.5 Compensating for the Frame Distortions . . . . . . . . . . . 91

5.5.1 Influence of Relative Motion . . . . . . . . . . . . . . . . . . . 92

5.5.2 Motion Distortions Model . . . . . . . . . . . . . . . . . . . . 92

5.5.3 Velocity Computation . . . . . . . . . . . . . . . . . . . . . . 94

5.5.4 Soft Tissue Deformations . . . . . . . . . . . . . . . . . . . . 95

5.6 Efficient Scattered Data Approximation . . . . . . . . . . . . 95

5.6.1 Discrete Shepard’s Like Method . . . . . . . . . . . . . . . . 96

5.6.2 Mosaic Construction . . . . . . . . . . . . . . . . . . . . . . . 97

5.7 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

5.7.1 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . 98

5.7.2 In Vivo Studies . . . . . . . . . . . . . . . . . . . . . . . . . . 104

5.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

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标签: 图象拼接 图像配准 共聚焦显微镜 
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