A Hybrid Method Based on Fuzzy Clustering and Local Region-Based Level Set for Segmentation of Inhomogeneous Medical Images

  • Maryam RastgarpourEmail author
  • Jamshid Shanbehzadeh
  • Hamid Soltanian-Zadeh
Systems-Level Quality Improvement
Part of the following topical collections:
  1. Systems-Level Quality Improvement


medical images are more affected by intensity inhomogeneity rather than noise and outliers. This has a great impact on the efficiency of region-based image segmentation methods, because they rely on homogeneity of intensities in the regions of interest. Meanwhile, initialization and configuration of controlling parameters affect the performance of level set segmentation. To address these problems, this paper proposes a new hybrid method that integrates a local region-based level set method with a variation of fuzzy clustering. Specifically it takes an information fusion approach based on a coarse-to-fine framework that seamlessly fuses local spatial information and gray level information with the information of the local region-based level set method. Also, the controlling parameters of level set are directly computed from fuzzy clustering result. This approach has valuable benefits such as automation, no need to prior knowledge about the region of interest (ROI), robustness on intensity inhomogeneity, automatic adjustment of controlling parameters, insensitivity to initialization, and satisfactory accuracy. So, the contribution of this paper is to provide these advantages together which have not been proposed yet for inhomogeneous medical images. Proposed method was tested on several medical images from different modalities for performance evaluation. Experimental results approve its effectiveness in segmenting medical images in comparison with similar methods.


Medical image segmentation Level set Fuzzy clustering Intensity inhomogeneity Automatic segmentation 



Computed tomography




Fuzzy C-Means


Fast two cycle


Fuzzy local information c-means


Kernel-based fuzzy c-means


Local binary fitting level set evolution


level set evolution with bias field estimation


medical image segmentation


magnetic resonance imaging


mean square error


mean sum of square distance


region of interest


segmentation in chest radiographs


spatial fuzzy C-means


White matter


Ethical standards

The authors declare that the experiments comply with the current laws of the country in which we were performed.

Conflict of interest

The authors have no actual or potential conflict of interest including any financial, personal, or other relationships with other people or organizations to disclose.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Maryam Rastgarpour
    • 1
    Email author
  • Jamshid Shanbehzadeh
    • 2
  • Hamid Soltanian-Zadeh
    • 3
    • 4
  1. 1.Department of Computer Engineering, Faculty of Engineering, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Computer Engineering, Faculty of EngineeringKharazmi University (TarbiatMoallem University)TehranIran
  3. 3.Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of EngineeringUniversity of TehranTehranIran
  4. 4.Image Analysis LabRadiology Department, Henry Ford Health SystemDetroitUSA

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