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Automatic Segmentation of Neonatal Images Using Convex Optimization and Coupled Level Set Method

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Medical Imaging and Augmented Reality (MIAR 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6326))

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Abstract

Accurate segmentation of neonatal brain MR images remains challenging mainly due to poor spatial resolution, low tissue contrast, high intensity inhomogeneity. Most existing methods for neonatal brain segmentation are atlas-based and voxel-wise. Although parametric or geometric deformable models have been successfully applied to adult brain segmentation, to the best of our knowledge, they are not explored in neonatal images. In this paper, we propose a novel neonatal image segmentation method, combining local intensity information, atlas spatial prior and cortical thickness constraint, in a level set framework. Besides, we also provide a robust and reliable tissue surfaces initialization for our proposed level set method by using a convex optimization technique. Validation is performed on 10 neonatal brain images with promising results.

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Wang, L., Shi, F., Gilmore, J.H., Lin, W., Shen, D. (2010). Automatic Segmentation of Neonatal Images Using Convex Optimization and Coupled Level Set Method. In: Liao, H., Edwards, P.J."., Pan, X., Fan, Y., Yang, GZ. (eds) Medical Imaging and Augmented Reality. MIAR 2010. Lecture Notes in Computer Science, vol 6326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15699-1_1

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  • DOI: https://doi.org/10.1007/978-3-642-15699-1_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15698-4

  • Online ISBN: 978-3-642-15699-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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