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Segmentation of 3-D MRI Brain Images Using Information Propagation

  • Jianzhong Wang
  • Jun Kong
  • Yinghua Lu
  • Jingdan Zhang
  • Baoxue Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4091)

Abstract

This paper presents an integrated method for adaptive segmentation of brain tissues in three-dimensional (3-D) MRI (Magnetic Resonance Imaging) images. The method intends to do the volume segmentation in a slice-by-slice manner. Firstly, some slices in the volume are segmented using an automatic algorithm composed of watershed, fuzzy clustering (Fuzzy C-Means) and re-segmentation. Then their adjacent slices can be segmented conveniently by propagating the information of them. The information is consisted of watershed lines and thresholds obtained from the re-segmentation approach. This integrated approach yields a robust and precise segmentation. The efficacy of the proposed algorithm is validated using extensive experiments.

Keywords

Segmentation Result Watershed Algorithm Medical Image Segmentation Adjacent Image Volume Segmentation 
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 2006

Authors and Affiliations

  • Jianzhong Wang
    • 1
    • 2
  • Jun Kong
    • 1
    • 2
  • Yinghua Lu
    • 1
  • Jingdan Zhang
    • 1
  • Baoxue Zhang
    • 2
  1. 1.Computer SchoolNortheast Normal UniversityChangchunChina
  2. 2.Key Laboratory for Applied Statistics of MOEChina

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