Brain MRI Segmentation and Bias Correction Model Based on Improved FCM with Non-local Information

The technology of magnetic resonance(MR) image can be used for auxiliary diagnoses of diseases.However,some image mechanisms often make images contaminated by noise or bias field,which makes the traditional fuzzy C means(FCM) algorithm difficult to obtain good segmentation results.For that,in the paper,we proposed a novel model based on FCM which combines segmentation and bias correction.Firstly,we take the bias field into the model to reduce the effect of intensity inhomogeneity;secondly,integrating the non-local information into the model can reduce the impact of noise as well as keep the image structures;finally,we introduce membership regular term to obtain crisp membership,so that the effect of membership at the transition area can be reduced,and the result of classification can be improved.Experimental results of the brain MR images show that the proposed method can reduce the impact of noise and bias field can be recovered in the process of segmentation,then obtain better segmentation results as well as the bias estimation in an accurate way.