A Fuzzy Kohonen’s Competitive Learning Algorithm for 3D MRI Image Segmentation

  • Jun Kong
  • Jianzhong Wang
  • Yinghua Lu
  • Jingdan Zhang
  • Jingbo Zhang
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 345)


Kohonen’s self-organizing feature map (SOFM) is a two-layer feed-forward competitive learning network, and has been used as a competitive learning clustering algorithm in brain MRI image segmentation. However, most brain MRI images always present overlapping gray-scale intensities for different tissues. In this paper, fuzzy methods are integrated with Kohonen’s competitive algorithm to overcome this problem (we will name the algorithm F_KCL). The F_KCL algorithm fuses the competitive learning with fuzzy c-means (FCM) cluster characteristic and can improve the segment result effectively. Moreover, in order to enhancing the robustness to noise and outliers, a kernel induced method is exploited in our study to measure the distance between the input vector and the weights (KF_KCL). The efficacy of our approach is validated by extensive experiments using both simulated and real MRI images.


Segmentation Result Radial Basis Function Kernel Fuzzy Method Neuron Excitation Segment Result 
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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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jun Kong
    • 1
    • 2
  • Jianzhong Wang
    • 1
    • 2
  • Yinghua Lu
    • 1
  • Jingdan Zhang
    • 1
  • Jingbo Zhang
    • 1
  1. 1.Computer SchoolNortheast Normal UniversityChangchun, Jilin ProvinceChina
  2. 2.Key Laboratory for Applied Statistics of MOEChina

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