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

  • Li Wang
  • Feng Shi
  • John H. Gilmore
  • Weili Lin
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6326)

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.

Keywords

White Matter Gray Matter Segmentation Result Automatic Segmentation Neonatal Brain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Li Wang
    • 1
    • 2
  • Feng Shi
    • 2
  • John H. Gilmore
    • 3
  • Weili Lin
    • 4
  • Dinggang Shen
    • 2
  1. 1.School of Computer Science & TechnologyNanjing University of Science and TechnologyChina
  2. 2.IDEA Lab, Department of Radiology and BRICUniversity of North Carolina at Chapel HillUSA
  3. 3.Department of PsychiatryUniversity of North Carolina at Chapel HillUSA
  4. 4.MRI Lab, Department of Radiology and BRICUniversity of North Carolina at Chapel HillUSA

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