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

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112 Chapter 7. Airway Tree Segmentation objects are shown in Fig. 7.2(c). (a) (b) (c) (d) Figure 7.2: Intermediate results of the tube centerline extraction method. The tube likeliness for the computed centerline points is shown as intensity value, and the extracted centerlines are drawn in green. (a) Tube-likeliness for larger structures. (b) Tube-likeliness for thin low contrast structures. (c) Extracted tube centerlines. (d) Segmentation result showing the size of the airways. Tube segmentation: After identification of the tubular structures, they are segmented using the method as described in Section 4.2. The segmentation results for individual tubular structures are shown in Fig. 7.3. Note that the combination of all individually segmented airway branches provides a valid segmentation of the complete airway tree although the centerlines are not necessarily connected. (a) (b) (c) (d) Figure 7.3: Inverse gradient flow tracking tube segmentation applied to extracted tubular structures. (a) Extracted centerlines showing 4 selected centerlines. (b) Segmentations associated with the selected centerlines. (c) Combined results of all tubular structures. (d) MinIP of smoothed dataset for comparison. Post processing: For the actual identification of the airway tree, the largest connected segmented component is extracted and other segmented tubular structures are discarded.
7.2. Methods 113 Additionally, the extracted centerlines are dilated and added to the segmentation result to assure 6-connected segmentation results. Parameters: The following set of parameters is used for segmentation of the datasets: σ = 0.5mm, F max = 200, µ = 5 for the GVF using 500 iterations, t high = 0.5, t low = 0.1, t s = 5, and t m = 0.5. 7.2.2 Airway Tree Reconstruction Based on Multi-Scale Tube Detection In this section, we present an automated approach for the reconstruction of airway trees. It is based no our general approach for segmentation of branched tubular networks as outlined in Section 1.2 to achieve a high robust against local disturbances which can result from disease or imaging artifacts, for example. The method consists of three main processing steps: preprocessing, extraction of tubular objects, and a grouping and linkage of the identified tubular objects to reconstruct the airway tree. Fig. 7.4 illustrates the individual processing steps by showing intermediate results. Preprocessing: To fully automate the approach for airway detection, the input CT dataset is preprocessed to discard non lung tissue and to restrict the search area for tubular structures. Therefor, a rough lung mask is generated by using thresholding (< −700HU), connected component analysis, and morphological closing with a ball structuring element with a radius of 10 voxels. All voxels outside this lung mask or with a value larger than −700HU in the original dataset are set to −700HU. The resulting dataset (Fig. 7.4(a)) was used as input for the later steps of the method. Extraction of tubular structures: For extraction of tubular structures, the tube detection filter of Pock et al. [113] as described in Section 2.2.2 is utilized in combination with the height-ridge traversal with hysteresis-thresholding for centerline extraction as described in Section 2.4. The method of Pock et al. was slightly adapted as for thin airways the surrounding is not necessarily symmetric and the utilized symmetry criterion adversely influences the tube detection. Therefore, for these structures (radius ≤ 0.5mm), the variance-based symmetry criterion is discarded. The tube detection filter response is shown in Fig. 7.4(b). To discard short spurious responses of the tube detection filter (noise), all centerlines with an accumulated tube detection filter response below t conf are discarded. Figs. 7.4(c) and (d) depict the resulting descriptions of the identified tubular structures. Fig. 7.4(c) shows only the centerline information, while in Fig. 7.4(d) also the
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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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48 Chapter 3. Grouping and Linkage
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Page 149 and 150: 8.1. Conclusion 127 arteries - info
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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,
Page 173: BIBLIOGRAPHY 151 [171] Yi, J. and R
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Segmentation of 3D Tubular Tree Structures in Medical Images ...

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