Segmentation of 3D Tubular Tree Structures in Medical Images ...

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Contents 1 Introduction 1 1.0.1 Requirements and Problems . . . . . . . . . . . . . . . . . . . . . . . 3 1.0.2 Representations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.0.3 Evaluation Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.1.1 Models, Features, and Extraction Schemes . . . . . . . . . . . . . . . 7 1.1.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.1.3 Vessel Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.2 A General Approach for Segmentation of Branched Tubular Networks . . . 11 1.3 Overview and Contributions of the Work . . . . . . . . . . . . . . . . . . . . 13 2 Extraction of Tubular Structures 17 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.2 Tube Detection Filters in Gaussian Scale Space . . . . . . . . . . . . . . . . 18 2.2.1 Central Medialness Functions . . . . . . . . . . . . . . . . . . . . . . 19 2.2.2 Offset Medialness Functions . . . . . . . . . . . . . . . . . . . . . . . 21 2.3 Tube Detection using Gradient Vector Flow . . . . . . . . . . . . . . . . . . 23 2.3.1 Combination with Central Medialness Function . . . . . . . . . . . . 27 2.3.2 Combination with Offset Medialness Function . . . . . . . . . . . . . 27 2.3.3 Adaption to Varying Background Conditions . . . . . . . . . . . . . 28 2.4 Centerline Extraction using Ridge Traversal . . . . . . . . . . . . . . . . . . 30 2.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.5.1 Synthetical Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.5.2 Clinical Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.6 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3 Grouping and Linkage into Connected Networks 47 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2 Structure Based Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.3 Image Based Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 xiii
Page 1: Graz University of Technology Insti
Page 5 and 6: Abstract The segmentation of tubula
Page 7 and 8: Kurzfassung Die Segmentierung tubul
Page 9: Acknowledgements I am deeply gratef
Page 14 and 15: xiv CONTENTS 3.4.1 Structural Corre
Page 17 and 18: List of Figures 1.1 Examples of 3D
Page 19: LIST OF FIGURES xix 7.7 Emphysema l
Page 23 and 24: Chapter 1 Introduction Contents 1.1
Page 25 and 26: 3 1.0.1 Requirements and Problems T
Page 27 and 28: 5 Graph-based representations: On a
Page 29 and 30: 1.1. Related Work 7 work are discus
Page 31 and 32: 1.1. Related Work 9 by iterative pr
Page 33 and 34: 1.2. A General Approach for Segment
Page 35 and 36: 1.3. Overview and Contributions of
Page 37 and 38: 1.3. Overview and Contributions of
Page 39 and 40: Chapter 2 Extraction of Tubular Str
Page 41 and 42: 2.2. Tube Detection Filters in Gaus
Page 43 and 44: 2.2. Tube Detection Filters in Gaus
Page 45 and 46: 2.3. Tube Detection using Gradient
Page 53 and 54: 2.5. Experiments 31 height-ridge tr
Page 55 and 56: 2.5. Experiments 33 (a) Datasets fr
Page 57 and 58: 2.5. Experiments 35 However, the ap
Page 59 and 60: 2.5. Experiments 37 (a) 3d volume r
Page 61 and 62: 2.6. Discussion and Conclusion 39 e
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2.6. Discussion and Conclusion 41 a
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2.6. Discussion and Conclusion 43 (
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Chapter 3 Grouping and Linkage into
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3.2. Structure Based Approach 49 sk
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3.3. Image Based Approach 51 Figure
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3.3. Image Based Approach 53 can be
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3.4. Experiments 55 (a) MIP of data
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3.4. Experiments 57 (a) (b) (c) (d)
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3.4. Experiments 59 (a) Volume rend
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3.4. Experiments 61 junction with t
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3.5. Dicussion and Conclusion 63 3.
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Chapter 4 Tube Segmentation Content
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4.2. Inverse Gradient Vector Flow T
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4.3. Graph Cut Based Tree Segmentat
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4.4. Experiments 71 representations
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4.4. Experiments 73 (a) Original da
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78 Chapter 5. Liver Vascular Tree S
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Chapter 6 Coronary Artery Tree Extr
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6.2. Method 99 (a) (b) (c) (d) (e)
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6.2. Method 101 (a) (b) (c) (d) Fig
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6.3. Evaluation and Results 103 - b
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6.3. Evaluation and Results 105 Tab
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6.5. Conclusion 107 The use of the
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110 Chapter 7. Airway Tree Segmenta
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126 Chapter 8. Conclusion and Outlo
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128 Chapter 8. Conclusion and Outlo
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Appendix A List of Publications In
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Appendix B List of Acronyms a.k.a.
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136 [11] Bennink, H. E., van Assen,
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138 [32] Dou, X., Wu, X., Wahle, A.
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140 [52] Hassouna, M., Farag, A., H
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142 [73] Law, M. and Chung, A. (200
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144 [96] McInerney, T. and Terzopou
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146 [119] Reinbacher, C., Pock, T.,
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148 [140] Tankyevych, O., Talbot, H
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150 [160] Wiemker, R., Bülow, T.,
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Segmentation of 3D Tubular Tree Structures in Medical Images ...

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