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

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36 Chapter 2. Extraction of Tubular Structures have a parameter that can be adapted to the expected contrast. As shown from the results, with the methods of Krissian and Pock the responses depend linearly on the contrast, while with the other approaches the response saturates above the expected contrast and starts decreasing with lower contrast than the expected one. One may argue that the methods of Krissian and Pock do not require an adaption to the expected contrast, however, on the other side this implies that for extraction of the tubular structures it is necessary to adapt thresholds on the TDF response appropriately to achieve a discrimination from noise responses. Morover, because of this behavior the methods of Krissian and Pock also produce strong responses to structures with a strong contrast, even in case the structure itself strongly deviates from a tubular shape. In contrast to that, the method of Frangi and the GVF-based method produce normalized results allowing to use the same set of parameters for extraction of the actual tubular structures. Varying noise level: The datasets provided by Aylward et al. ‡ [2], see Fig. 2.10, contains a tortuous, branching, tubular object with vanishing radius. The contrast between the tubular object and the background ranges from 100 at the center of the tube to 50 at the tube’s edge. The datasets were corrupted with additive Gaussian noise with increasing standard deviations η of 10, 20, 40 and 80. “The η = 20 data is representative of the noise level in MR and CT data. The η = 40 data more closely resembles the noise magnitude of ultrasound data. [...] The η = 80 images are well beyond any worst case number [...] for any clinically acceptable MRA, CT, or ultrasound data.”[2]. For Frangi’s method and the GVF-based methods, the noise-sensitivity parameters, c and F max , respectively, were adapted on the low noise dataset (η = 10). As already mentioned above, the methods of Krissian and Pock do not allow controlling the influence of contrast on the filter response. As a result, with these methods the response to image noise increases with an increasing noise level. The GVF-based approaches produce clean responses at the centers of the tubular objects even under high (clinically acceptable) noise levels (η = 10, 20, and 40), although for noise reduction only a Gaussian smoothing with a very small variance (σ = 1.0) was used. This shows that in practice only a Gaussian smoothing with a very small variance is necessary. Computation of gradient information on a large scale as done by the Gaussian scale space based methods is not necessary to account for image noise. However, in case of insufficient noise suppression the GVF-based method with the central medialness function produces unsatisfying results, while the GVF-based method with the offset medialness function shows a slightly better behavior, as shown in ‡ http://ij.itk.org/midas/item/view/1065
2.5. Experiments 37 (a) 3d volume rendering 100 50 1000 500 0 0 0 20 40 60 80 100 120 1400 20 40 60 80 100 120 140 (b) 1d cross section showing contrast (c) Response of Frangi’s method [44]. 5 10 5 0 0 0 20 40 60 80 100 120 1400 20 40 60 80 100 120 140 (d) Response of Krissian’s method [70] (e) Response of Pock’s method [114]. 20 500 10 0 0 0 20 40 60 80 100 120 1400 20 40 60 80 100 120 140 (f) Response of GVF-based tube detection with central medialness (Section 2.3.2). (g) Response of GVF-based tube detection with offset medialness (Section 2.3.2). Figure 2.9: Tubular structure with vanishing contrast and responses of different TDFs. The x-axis indicates the location and the y-axis the contrast/response. The dotted line in (b) indicates the contrast for which the parameters were adapted. Figs. 2.10(e) and (f) on the very right. 2.5.2 Clinical Datasets In this section, we apply the three methods to three medical volume datasets and show the impact of the results obtained on the synthetic datasets on clinical datasets. CT angiography: In the top row of Fig. 2.11, a CT angiography image and enlarged subregions of challenging areas are shown. The main problems for TDFs with this kind of dataset are the detection of very thin low contrast vessels, diffuse edges, and closely adjacent vessels. Note that Frangi’s and Krissian’s methods were specifically designed for angiography images.
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Graz University of Technology Insti
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Abstract The segmentation of tubula
Page 7 and 8: Kurzfassung Die Segmentierung tubul
Page 9: Acknowledgements I am deeply gratef
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5.3. Evaluation and Results 87 (a)
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5.4. Discussion 93 results. Selle
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5.5. Conclusion 95 a prior to const
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98 Chapter 6. Coronary Artery Tree
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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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