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

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58 Chapter 3. Grouping and Linkage into Connected Networks (a) Volume rendering of dataset. (b) GVF-based grouping and linkage (Section 3.3). (c) Structure based grouping and linkage (Section 3.2). (d) Mutliscale tube detection and simple linkage. (e) Hassouna’s skeletonization approach. (f) Bouix’s skeletonization approach. (g) Palagyi’s skeletonization approach. Figure 3.5: Curve skeletons of an airway tree. Green: Result of GVF-based grouping and linkage (Section 3.3). Red: Other approaches. directly from the gray value image. The skeletonization approaches are that of Hassouna et al. [53–55], Bouix et al. [15], and Palagyi et al. [110]. Hassouna’s approach also uses the GVF similarly to our approach; the other two methods were specifically developed for tubular objects. As an alternative method that tries to extract centerlines directly from gray value images we decided to use a combination of the methods of Krissian et al. [70] (for a bottom-up detection of tubular structures) and Bullitt et al. [18] (for grouping the single tubular objects into tree structures and an extraction of the complete CS). For all approaches the authors default parameters were used. For our grouping/linkage meth-
3.4. Experiments 59 (a) Volume rendering of dataset. (b) GVF-based grouping and linkage (Section 3.3). (c) Structure based grouping and linkage (Section 3.2). (d) Mutliscale tube detection and simple linkage. (e) Hassouna’s skeletonization approach. (f) Bouix’s skeletonization approach. (g) Palagyi’s skeletonization approach. Figure 3.6: Curve skeletons of an aorta containing a severe stenosis due to calcification. Green: Result of GVF-based grouping and linkage (Section 3.3). Red: Other approaches. ods, the tubular structures were obtained from the datasets using the GVF-based TDF with the offset medialness function as presented in Section 2.3.2. For the structural tree reconstruction method (Section 3.2) the required root elements were selected manually.
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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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Page 99 and 100: Chapter 5 Liver Vascular Tree Segme
Page 101 and 102: 5.2. Method 79 (a) MIP of the datas
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Page 120 and 121: 98 Chapter 6. Coronary Artery Tree
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Chapter 7 Airway Tree Segmentation
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7.2. Methods 111 shown in Fig. 7.1.
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7.2. Methods 113 Additionally, the
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7.3. Evaluation and Results 115 Gro
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7.3. Evaluation and Results 117 Tab
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7.4. Discussion 119 Figure 7.5: “
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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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Segmentation of 3D Tubular Tree Structures in Medical Images ...

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