臺灣博碩士論文加值系統

近年來，影像套合技術已廣泛的應用在醫學影像上，並且有了不錯的成果。影像套合技術是關於兩張影像在空間上作轉換及對映，它的應用包含：將不同影像模式中的相同影像物件作適當的套合；以及校正原本在時間上應屬連續的影像，以減低它們在掃描中過程中因為物體移動所產生的變異。此外影像套合技術又可分為剛性與非剛性兩種。在這份論文中，我們分別提出幾項應用於乳房超音波影像之剛性與非剛性的影像套合研究。
在剛性套合的研究中，我們提出一種新的演算法：它可以整合經由超音波掃描下來的個別影像，並從中取出有用的資訊，然後利用這些資訊及剛性影像套合技術，將數張影像縫合成一張全乳房影像。這張縫合後的全影像不但可以展現出整個乳房的結構，亦可協助醫師減輕在校對工作上的負擔，甚至還可以應用在電腦輔助診斷的系統中，做出腫瘤良惡性之檢測。其次，我們還利用非剛性影像套合技術來分析腫瘤的變異。通常腫瘤的形狀會因為超音波探頭的壓迫而產生變形，因此非剛性影像套合技術即可用來分析腫瘤在探頭施壓前後的變化，並且透過一些統計方法來協助判斷腫瘤的良惡性。從目前進行中的結果，皆可顯示這些研究已有不錯的成效。最後，我們也提出一些將來所要研究的方向。

Recently, image registration has been applied popularly into medical imaging and several superior results have also been demonstrated. Image registration technique refers to a process of determining a spatial transform mapping on one image into another. These applications involving image registration include registering images of the same subject from different modalities and aligning temporal sequences of images to compensate for motion of the subject between scans. Besides, image registration can be divided into rigid and non-rigid registration, respectively. In this thesis, we will present several studies respectively related to rigid and non-rigid image registration techniques on breast ultrasound.
In the study for rigid registration, we proposed a newly developed algorithm that can integrate the useful information from the separately scanned ultrasound images and stitch these images into a full-view breast image using the rigid-body image registration. The produced full-view images can reveal the breast anatomy and assists physicians to reduce extra manual adjustment. Moreover, these full-view images also can be applied into the computer-aided diagnosis (CAD) system to detect the suspicious tumor regions. Second, we utilized non-rigid image registration modality on the analysis of tumor deformation. Because the shape of a tumor may be altered due to the stress caused by the ultrasound probe, the non-rigid image registration can be utilized to analyze the variation of tumor between pre- and post-compression and determine whether the tumor is benign or not with several statistical materials. The current results show that these proposed studies have great achievements. At last, we also propose several topics in future researches.

中文摘要 i
Abstract ii
Acknowledgement iv
List of Figures vii
List of Tables xi

Chapter 1 Introduction 1
1.1 Research Motivation 1
1.2 Issue Description 2
1.2.1 Rigid and Non-rigid Image Registration 2
1.2.2 Ultrasound Image Registration 4
1.2.3 Image Stitching 6
1.2.4 Subregion Registration 8
1.3 Thesis Organization 8
Chapter 2 Review of Related Works 11
2.1 Data Acquisition for Ultrasound Image Data Sets 11
2.1.1 The First Ultrasonic Machine: ASU-1004 11
2.1.2 The Second Ultrasonic Machine: Voluson-530 13
2.2 Image Registration Methodology 15
2.3 Image Registration Measures 19
2.3.1 Minimizing Intensity Difference 19
2.3.2 Correlation Techniques 20
2.3.3 Information Theoretical Techniques 21
Chapter 3 Image Stitching for Three-Pass Whole Breast Ultrasound 26
3.1 Introduction 26
3.2 Data Acquisition and Related Works 28
3.2.1 Whole Breast Ultrasound 29
3.2.2 Image Stitching Measures 29
3.3 The Proposed Stitching Methods 32
3.3.1 Mutual Information-based Image Stitching Step 33
3.3.2 The Seamless Joint-line Step 38
3.3.3 The Average Merging Method 42
3.4 Experimental Protocol and Results 43
3.5 Discussion 49
3.6 Conclusion 51
Chapter 4 Rapid Image Stitching and Computer-aided Diagnosis for Whole Breast Ultrasound Image 55
4.1 Introduction 55
4.2 Materials and Methods 57
4.2.1 Data Acquisition 57
4.2.2 Image Stitching Procedure 58
4.2.3 Automatic Screening System 63
4.3 Experimental Protocol and Results 75
4.4 Discussion 80
4.4.1 A Discussion on the Parameters and Thresholds 80
4.4.2 A Discussion on the Proposed CAD System 80
4.5 Conclusion 81
Chapter 5 2-D Ultrasound Strain Images for Breast Cancer Diagnosis Using Nonrigid Subregion Registration 83
5.1 Introduction 83
5.2 Materials and Methods 85
5.2.1 Data Acquisition 85
5.2.2 Image Segmentation Step 87
5.2.3 The Proposed Subregion Registration Procedure 90
5.2.4 Classification and Statistical Analysis 99
5.3 Experimental Protocol and Results 102
5.4 Discussion 105
5.5 Conclusion 107
Chapter 6 Future Works 109
6.1 Introduction 109
6.2 Feature-based Image Registration 109
6.3 3-D Registration for 3-D Ultrasound Strain Images 112
6.4 Conclusion 114
Chapter 7 Conclusion 116
References 118

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