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

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72 Chapter 4. Tube Segmentation (a) Volume rendering of dataset. (b) Structure of the whole airway tree. (c) Structure of a selected centerline path. (d) Shape prior based on centerlinepoints and radius estimates. (e) Graph cut segmentation. (f) GVF-based segmentation. Figure 4.3: Segmentation of an airway tree in CT data. Top row: Dataset, reconstructred tree, and a selected centerline path. Bottom row: Segmentations showing the segmentation of the whole tree (red) and the segmentation associated with the selected centerline path (green). Quantitative results: For quantitative evaluation, the segmentation results were compared with the reference segmentations and some well known segmentation performance measures were obtained. As the graph cut segmentation and the GVF-based method
4.4. Experiments 73 (a) Original dataset. (b) Shape prior. (c) Graph cut segmentation. (d) GVF-based segmentation. Figure 4.4: Segmentation of a diseased abdominal aorta. Bottom row: slice based visualization. Tow row: 3D visualization. show a qualitative difference – the graph cut segmentation includes calcifications while the GVF-based method does not – the segmentation results were also compared to reference segmentations including/excluding calcifications. As segmentation performance measures, the segmentation overlap, the unsigned volume error, and the unsigned surface distance are used. Given the segmentation result S and the reference segmentation R, the segmentation overlap Φ is defined as Φ = 2V (S ∈ R)/(V (S)+V (R)) where V corresponds to the volume, the unsigned volume error Υ is Υ = |V (S) − V (R)|/V (R), and the mean unsigned surface distance error µ d is the average distance of all surface points of S to the closest point on the surface R. The results on the four datasets are summarized in
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Graz University of Technology Insti
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Abstract The segmentation of tubula
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Kurzfassung Die Segmentierung tubul
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Acknowledgements I am deeply gratef
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Contents 1 Introduction 1 1.0.1 Req
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CONTENTS xv 8 Conclusion and Outloo
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xviii LIST OF FIGURES 4.2 Shape pri
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List of Tables 3.1 Average centerli
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2 Chapter 1. Introduction (a) Porta
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4 Chapter 1. Introduction (a) Volum
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6 Chapter 1. Introduction (a) Inacc
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8 Chapter 1. Introduction features
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10 Chapter 1. Introduction 1.1.2 Di
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12 Chapter 1. Introduction Input vo
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14 Chapter 1. Introduction part: Me
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16 Chapter 1. Introduction in the l
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18 Chapter 2. Extraction of Tubular
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20 Chapter 2. Extraction of Tubular
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Page 70 and 71: 48 Chapter 3. Grouping and Linkage
Page 88 and 89: 66 Chapter 4. Tube Segmentation Onl
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Page 99 and 100: Chapter 5 Liver Vascular Tree Segme
Page 101 and 102: 5.2. Method 79 (a) MIP of the datas
Page 103 and 104: 5.3. Evaluation and Results 81 5.3.
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Page 115 and 116: 5.4. Discussion 93 results. Selle
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Page 120 and 121: 98 Chapter 6. Coronary Artery Tree
Page 131 and 132: Chapter 7 Airway Tree Segmentation
Page 133 and 134: 7.2. Methods 111 shown in Fig. 7.1.
Page 135 and 136: 7.2. Methods 113 Additionally, the
Page 137 and 138: 7.3. Evaluation and Results 115 Gro
Page 139 and 140: 7.3. Evaluation and Results 117 Tab
Page 141 and 142: 7.4. Discussion 119 Figure 7.5: “
Page 143 and 144: 7.5. Conclusion 121 Comparison to o
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7.5. Conclusion 123 Figure 7.6: Exa
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Chapter 8 Conclusion and Outlook Co
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8.1. Conclusion 127 arteries - info
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8.2. Directions for Future Work 129
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132 Chapter A. List of Publications
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BIBLIOGRAPHY 135 Bibliography [1] A
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BIBLIOGRAPHY 137 [21] Choyke, P., Y
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BIBLIOGRAPHY 139 [41] Florin, C., P
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BIBLIOGRAPHY 141 [63] Kitslaar, P.
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BIBLIOGRAPHY 143 [85] Lindeberg, T.
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BIBLIOGRAPHY 145 [108] O’Donnell,
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BIBLIOGRAPHY 147 [129] Schmugge, S.
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BIBLIOGRAPHY 149 [150] van Bemmel,
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BIBLIOGRAPHY 151 [171] Yi, J. and R
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