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A Study on the Application of Fuzzy Information Seeded Region Growing in Brain MRI Tissue Segmentation

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  • Chuin-Mu Wang
  • Geng-Cheng Lin

Abstract

After long-term clinical trials, MRI has been proven to be used in humans harmlessly, and it is popularly used in medical diagnosis. Although MR is highly sensitive, it provides abundant organization information. Therefore, how to transform the multi-spectral images which is easier to be used for doctor’s clinical diagnosis. In this thesis, the fuzzy bidirectional edge detection method is used to solve conventional SRG problem of growing order in the initial seed stages. In order to overcome the problems of the different regions, although it is the same Euclidean distance for region growing and merging process stages, we present the peak detection method to improve them. The standard deviation target generation process (SDTGP) is applied to guarantee the regions merging process does not cause over- or undersegmentation. Experimental results reveal that FISRG segments a multispectral MR image much more effectively than FAST and -means.

Suggested Citation

  • Chuin-Mu Wang & Geng-Cheng Lin, 2014. "A Study on the Application of Fuzzy Information Seeded Region Growing in Brain MRI Tissue Segmentation," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-11, May.
    • Handle: RePEc:hin:jnlmpe:290607
    DOI: 10.1155/2014/290607
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