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

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110 Chapter 7. Airway Tree Segmentation on density/gray-value information for airway segmentation, some approaches focus on airway candidate detection using mathematical morphology [37], template matching techniques [136], or voxel classification based on different image descriptors [86, 122]. Many of the available approaches have problems in dealing with local disturbances (e.g., motion artifacts) or pathology (e.g., obstructed airway) which may result in incomplete airway segmentations. Graham et al. [50] addressed this problem using a different approach by building an airway tree from candidate airway branch segments by computing connection costs between branches and using graph theoretic approaches to extract the airway tree [50]. In this chapter, we present two different methods for the extraction of airways trees from CT datasets with different properties. The first method performs a segmentation of the airway tree based on the GVF, while the second method performs a reconstruction of the airway tree from detected/extracted tubular objects based on structural properties. Both approaches have been evaluated on a public database. Results as well as a comparison to the results achieved with other methods are presented and discussed. 7.2 Methods 7.2.1 Airway Tree Segmentation Based on Gradient Vector Flow In this section, we present an automated approach for segmentation of airway trees based on the GVF. The method produces accurate segmentations of the airway lumen and the obtained GVF field may be directly utilized to obtain a high-quality skeleton. The method follows the general approach for segmentation of branched tubular networks as outlined in Section 1.2. However, the grouping step is skipped as it is not necessary for this task. The method consists of four processing steps: deriving an appropriate initial vector field and applying the GVF, extraction of tubular structures from the GVF field, segmentation of the identified tubular structures using the GVF field, and post processing for identification of the airway tree. Intermediate processing results are depicted in Figs. 7.1, 7.2, and 7.3. Gradient Vector Flow: The extraction of the tubular objects as well as the segmentation parts utilize the GVF field V (x) resulting from the following initial vector field that is required if one is interested in dark structures as explained in Section 2.3: F n (x) = F (x) min(|F (x)|,F max) |F (x)| F max with F = −∇(G σ ⋆ I), where I is the original image and G σ is a Gaussian filter kernel at scale σ. An example of applying it to a thorax CT dataset is
7.2. Methods 111 shown in Fig. 7.1. (a) (b) (c) (d) (e) (f) (g) (h) Figure 7.1: Example of initial vector field and GVF field on a thorax CT dataset. (a) Minimum Intensity Projection (MinIP) of the dataset. (b) MinIP showing the Gauss-smoothed dataset with σ = 0.5 that was used to calculate the initial gradient F (x). (c) MinIP of the GVF magnitude M(x) inside the segmentation result. (d) Segmentation result; the axial cutting plane used in (e)-(h) is indicated by a black line. (e) Axial slice of the dataset showing part of the trachea and some thin low contrast airways. (f) Magnitude of initial vector field |F n (x)| before applying the GVF. (g) Magnitude of the GVF field |V (x)|. (h) Segmentation result. Extraction of tubular structures: In this GVF field, the tubular structures are identified using the offset medialness function based method as described in Section 2.3.2. This results in a tube-likeliness measure at the centerlines, as shown in Fig. 7.2(a). This information can be used for detection and centerline extraction of tubular objects. However, for thin low contrast airways, the response may fall off strongly, if their gradient-magnitude is too low so that they are not completely preserved in the GVF result (Figs. 7.1(f) and (g)). Applying the same procedure with a radius of 0.5 mm on the initial vector field F n (x) allows identification of these structures as shown in Fig. 7.2(b), and therefore, the maximum of both responses is utilized to produce a combined tube-likeliness volume. From this combined tube-likeliness image the centerlines of tubular structures are extracted using the hysteresis thresholding as described in Section 2.4. From these initial centerlines, short spurious centerlines with a length (below t s ) are discarded. In addition, centerlines with a mean tube-likeliness below t m are removed. The resulting centerlines of the tubular
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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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Page 169 and 170: BIBLIOGRAPHY 147 [129] Schmugge, S.
Page 171 and 172: BIBLIOGRAPHY 149 [150] van Bemmel,
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