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Information Journal Paper

Title

BRAIN MRI SEGMENTATION USING OPTIMIZED BCFCM BASE ON GA AND PSO

Pages

  3-13

Keywords

GAQ1
PSOQ2

Abstract

 Segmentation of medical images is one of the initial pre-processing for designing automated diagnosing systems. Magnetic Resonance Imaging (MRI) of the brain is associated with intensity uncertainty due to destructive artificial factors in the imaging process such as noise and Intensity Non-Uniformity (INU). As a result, SEGMENTATION of these images is a challenging issue. Due to uncertainty in BRAIN MRI, researchers have employed fuzzy methods for segmenting BRAIN MRI. BCFCM is one of the fuzzy SEGMENTATION methods in which information of neighboring pixels are also used for SEGMENTATION. This method has different parameters which inappropriate selection of that, greatly reduces the performance of the method. In this paper an optimized BCFCM with two structure is proposed for BRAIN MRI SEGMENTATION. In the optimization process, GA and PSO are used. Appropriate performance of the proposed method is demonstrated by simulation results of standard Brain-Web datasets using Tanimoto and Dice similarity measures.

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  • Cite

    APA: Copy

    GHASEMI, JAMAL, & ARGHAND, IMAN. (2016). BRAIN MRI SEGMENTATION USING OPTIMIZED BCFCM BASE ON GA AND PSO. JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT), 4(4), 3-13. SID. https://sid.ir/paper/245876/en

    Vancouver: Copy

    GHASEMI JAMAL, ARGHAND IMAN. BRAIN MRI SEGMENTATION USING OPTIMIZED BCFCM BASE ON GA AND PSO. JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT)[Internet]. 2016;4(4):3-13. Available from: https://sid.ir/paper/245876/en

    IEEE: Copy

    JAMAL GHASEMI, and IMAN ARGHAND, “BRAIN MRI SEGMENTATION USING OPTIMIZED BCFCM BASE ON GA AND PSO,” JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT), vol. 4, no. 4, pp. 3–13, 2016, [Online]. Available: https://sid.ir/paper/245876/en

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