Abstract
We address the problem of precise segmentation of 3D complex structure from high-contrast images. Particularly, we focus on the representation and application of prior knowledge in the 3D level set framework. We discuss the limitations of the popular prior shape model in this type of situations, and conclude that shape model only is not complete and effective if not augmented by high-level boundary and context features. We present the principle that global priors should not compete with local image forces at the same level, but should instead guide the evolving surface to converge to the correct local primitives, thus avoiding the common problems of leakage and local minima. We propose several schemes to achieve this goal, including initial front design, speed design, and the introduction of high-level context blockers.
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© 2005 Springer-Verlag Berlin Heidelberg
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Shen, H., Shi, Y., Peng, Z. (2005). Applying Prior Knowledge in the Segmentation of 3D Complex Anatomic Structures. In: Liu, Y., Jiang, T., Zhang, C. (eds) Computer Vision for Biomedical Image Applications. CVBIA 2005. Lecture Notes in Computer Science, vol 3765. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569541_20
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DOI: https://doi.org/10.1007/11569541_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29411-5
Online ISBN: 978-3-540-32125-5
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