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

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100 Chapter 6. Coronary Artery Tree Extraction at their centerlines, but for the very thin low-contrast vessels at the distal ends of the arteries the edge-information is not sufficiently strong and the response falls off completely. Compared to that, applying Frangi’s vesselness measure to the original vector field F n , the response for the thin low-contrast vessels just decreases, but it also produces responses of similar strength to some imaging artifacts/noise that locally may look similar to such thin vessels. Resolving this ambiguity in the tube detection filtering without a more sophisticated pre-processing for noise-suppression seems unrealistic. However, the usage of a hysteresis thresholding (see next paragraph) enables coping with this problem. Of course, applying Frangi’s vesselness measure to F n , does not allow detection of larger image structures. To enable an extraction of larger vessels and thin-low contrast vessels, the maximum of both filter responses – Frangi’s vesselness measure applied to the initial vector field F n and the GVF result V n – is used which is in the remainder of this chapter considered as the TDF response T (see Figs. 6.2(d), 6.1(b) and 6.1(c)). After the TDF step, centerlines for the individual tubular structures are extracted using the ridge traversal with hysteresis-thresholding as described in Section 2.4, and very short spurious responses are discarded by keeping only centerlines that have connected regions of a minimum length (length > l min ) above the upper threshold T (x) > t high . In Fig. 6.1(d) the extracted centerlines from the TDF response from Fig. 6.1(c) are shown. The outlined ridge traversal performs well in extracting the centerlines of the tubular objects also at the thin low-contrast ends of the vessels where the TDF response falls off considerably while still discarding short spurious responses. In this way major parts of the coronary artery centerline are already extracted, but the centerlines may break up in junction areas or regions where the vessels are partly indistinguishable from the background. Grouping and linkage: To account for these cases and to reconstruct the complete vascular trees, the single centerlines are grouped and linked together. Therefor, possible connections (and connection costs) between the centerlines are identified based on distance, angle, and gray value difference similarly to the structure based tree reconstruction and separation method as described in Section 3.2. However, for coronary artery trees no separation of interwoven tubular tree structures is necessary and the coronary artery tree structures can be obtained by constructing a minimum spanning forest (see [25], Chapter 23). Similarly to the structure based tree reconstruction method from Section 3.2, connection costs are computed between complete centerlines and not all centerline points of all tubular structures. The endpoints of single centerlines are considered and the ap-
6.2. Method 101 (a) (b) (c) (d) Figure 6.2: Tube detection step. For visualization only image regions in proximity of the coronary artery trees are shown. (a) MIP of the original dataset. (b) Tube detection result on the initial vector field F n . (c) Tube detection result on the normalized GVF field V n . (d) Combined tube detection result T . proach searches in the outward pointing tangent-direction of the centerline for potential continuations. Based on the centerline points coordinate x S and its tangent-direction t S connection costs to other centerline points x E are computed and used for the minimum spanning forest construction; possible connections with a too large gray value difference |I(x S ) − I(x E )| > d max are immediately rejected. The connection costs C(x S , t S , x E ) represent a trade-off between distance and angle similarly to the formulation used in Section 3.2: C(x S , t S , x E ) = ||x S − x E ||/exp(−∠( −−−→ x S x E , t S )/(2ρ 2 )) (6.1)
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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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48 Chapter 3. Grouping and Linkage
Page 72 and 73: 50 Chapter 3. Grouping and Linkage
Page 88 and 89: 66 Chapter 4. Tube Segmentation Onl
Page 90 and 91: 68 Chapter 4. Tube Segmentation obt
Page 92 and 93: 70 Chapter 4. Tube Segmentation ass
Page 94 and 95: 72 Chapter 4. Tube Segmentation (a)
Page 96 and 97: 74 Chapter 4. Tube Segmentation Tab
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.
Page 105 and 106: 5.3. Evaluation and Results 83 cent
Page 107 and 108: 5.3. Evaluation and Results 85 (a)
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
Page 145 and 146: 7.5. Conclusion 123 Figure 7.6: Exa
Page 147 and 148: Chapter 8 Conclusion and Outlook Co
Page 149 and 150: 8.1. Conclusion 127 arteries - info
Page 151: 8.2. Directions for Future Work 129
Page 154 and 155: 132 Chapter A. List of Publications
Page 157 and 158: BIBLIOGRAPHY 135 Bibliography [1] A
Page 159 and 160: BIBLIOGRAPHY 137 [21] Choyke, P., Y
Page 161 and 162: BIBLIOGRAPHY 139 [41] Florin, C., P
Page 163 and 164: BIBLIOGRAPHY 141 [63] Kitslaar, P.
Page 165 and 166: BIBLIOGRAPHY 143 [85] Lindeberg, T.
Page 167 and 168: BIBLIOGRAPHY 145 [108] O’Donnell,
Page 169 and 170: BIBLIOGRAPHY 147 [129] Schmugge, S.
Page 171 and 172: 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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