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

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74 Chapter 4. Tube Segmentation Table 4.1. The performance measures seem to be slightly better with the graph cut based segmentation method. However, with both of the methods an average unsigned surface distance of 0.5 ± 0.1mm and 0.6 ± 0.1mm, respectively, is achieved, which is lower than the average intra-slice resolution of these datasets. Table 4.1: Segmentation performance on diseased abdominal aorta datasets. Error measure Graph cut segmentation GVF based segmentation mean ± std.dev. mean ± std.dev. Segmentation overlap Φ 95.1 ± 0.9% 93.5 ± 0.5% Mean unsigned volume error Υ 2.1 ± 1.8% 7.6 ± 1.6% Mean unsigned surface distance µ d 0.5 ± 0.1mm 0.6 ± 0.1mm 4.5 Discussion and Conclusion In this chapter, we were concerned with the segmentation of tubular structures in case of a given rough shape prior. We presented a graph cut based method utilizing a shape prior (Section 4.3) and a GVF-based segmentation method (Section 4.2). In Section 4.4 we studied the robustness and behavior of both methods showing some of their properties. Both methods were capable of handling cases of abnormal tubular shapes and produced voxel-accurate segmentations in these cases showing a good correspondence with the expected results. Both methods showed robustness against major leakage due to the used shape prior or properties of the GVF, respectively. The main difference between both approaches is that the graph cut segmentation solves the segmentation in a globally optimal way, while the GVF-based approach segments according to the first distinct edge closest to the centerline. Which of the two different behaviors is desired may vary depending on the application. Regarding computation time, both approaches are computationally inexpensive. The GVF-based segmentation method (Section 4.2) shows similar characteristics as a conventional region grower. With the graph cut based method (Section 4.3) also only image regions in proximity of the tubular structures are considered for computation of the signed distances and the graph cut algorithm itself.
4.5. Discussion and Conclusion 75 (a) Original data. (b) Shape prior based on centerlinepoints and radius estimates. (c) Graph cut segmentation. (d) GVF-based segmentation. Figure 4.5: Segmentation of an airway tree showing segmentations in the axial slices along the centerline path (Fig. 4.3(c)) from the trachea to a distal end of the airway tree.
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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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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
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
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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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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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