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

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60 Chapter 3. Grouping and Linkage into Connected Networks Qualitative Results: In Figs. 3.5 and 3.6, the results achieved with our methods and the other methods on the clinical datasets are shown. Enlarged subwindows in regions around a junction and stenosis are shown in Figs. 3.7 and 3.8, respectively. The airway dataset (Figs. 3.5 and 3.7) demonstrates that the results of the different methods in the case of a single perfectly tubular structure are almost identical. Only the method utilizing the multi-scale tube detection filter shows some deviation. But at one of the junctions that shows a larger variation from the typical shape (it becomes more plate like) some approaches had problems to produce valid centerlines. In particular, the combined approach of Krissian and Bullit had problems since this combination does not guarantee centered paths in junction areas. Both our grouping/linkage methods show a reasonable behavior in this case and the centerlines overlap in the tubular parts completely as both are based on the same extracted tubular structures. Thus, the results only deviate in the branching areas. The completeness of the resulting CS regarding the structure of the trees achieved with the GVF-based approach is also comparable to that of the gold standard segmentation. (a) GVF-based grouping and linkage. (b) Structure based grouping and linkage (Section 3.3). (c) Mutliscale tube detection and simple linkage (Section 3.2). (d) Hassouna’s skeletonization approach. (e) Bouix’s skeletonization approach. (f) Palagyi’s skeletonization approach. Figure 3.7: Enlarged subwindow showing curve skeletons of an airway tree. Green: Result of GVF-based grouping and linkage (Section 3.3). Red: Other approaches. On the aorta dataset (Fig. 3.6), the different methods show major differences at the
3.4. Experiments 61 junction with the stenotic area (Fig. 3.8). Krissian’s multi-scale TDF was influenced by the calcification and thus the resulting centerline moved far away from the desired position. The GVF-based TDFs on the other hand are not sensitive to such kinds of disturbances. In proximity of the stenosis where the TDF filter was not able to identify a tubular structure, the GVF-based grouping/linkage was able to extract a medial curve that stayed centered with a quality comparable to that of the skeletonization approaches. The simpler structure-based grouping/linkage methods on the other hand closed the gap between the tubular structures along a straight line, showing a large deviation from a high quality skeleton. The skeletonization approaches of Bouix and Palagyi showed some sensitivity to surface noise in the gold standard segmentation and produced additional centerlines. The authors themselves mention this behavior and these spurious branches can be removed by appropriate pruning strategies. (a) GVF-based grouping and linkage (Section 3.3). (b) Structure based grouping and linkage (Section 3.2). (c) Mutliscale tube detection and simple linkage. (d) Hassouna’s skeletonization approach. (e) Bouix’s skeletonization approach. (f) Palagyi’s skeletonization approach. Figure 3.8: Enlarged subwindow showing 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.
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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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Page 99 and 100: Chapter 5 Liver Vascular Tree Segme
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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
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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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