Computer Science ›› 2015, Vol. 42 ›› Issue (10): 311-315.

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 Anti-noise BCFCM Algorithm for Brain MRI Segmentation

LUAN Fang-jun, ZHOU Jia-peng and ZENG Zi-ming   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Magnetic resonance imaging (MRI) of brain is an important tool for the clinical diagnosis of brain diseases.The accurate segmentation for brain tissues is one of the important parts.However,it is difficult to acquire the accurate segmentation results because of the noise and intensity inhomogeneities in MRI.Among the MRI segmentation me-thods,Bias-Corrected FCM (BCFCM) algorithm based on Fuzzy C-Means (FCM) algorithm utilizes the spatial information and estimation of intensity inhomogeneities which can deal with the problem caused by intensity inhomogeneities.Because the BCFCM algorithm fails to consider the high level noise when estimating intensity inhomogeneities,the segmentation results are not accurate enough.For the MRI of brain tissue segmentation,this paper proposed a fast segmentation method to remove the brain skull and its appendages during the image preprocessing.In addition,we proposed an improved algorithm based on the BCFCM algorithm.The improved BCFCM algorithm can automatically change the size of window in the objective function by estimating the noise level in the iterative processing.Besides,the Gaussian kernel in the object function was utilized to smooth the intensity inhomogeneities,and the estimation value of intensity inhomogeneities was limited by using an experimental threshold which can effectively avoid the incorrect estimation of intensity inhomogeneities in the segmentation results.The experimental results show that the proposed algorithm can not only effectively and accurately segment the brain tissues,but also deal with high level noise and intensity inhomogeneities.

Key words: Magnetic resonance imaging,Bias-corrected FCM,Noise estimation,Adaptive,Bias-limited

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