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Automated Medical Image Segmentation Based on FCM and LBF Model

The medical image segmentation based on Local Binary Fitting(LBF) model is sensitive to initial contour and merely available to single object.If its initial contour chosen manually is not suitable,the segmentation needs too much CPU time and sometimes is even unsuccessful.To overcome these disadvantages,an integrate Fuzzy C-means(FCM) clustering into LBF model is proposed for automated image segmentation.The image to be segmented is clustered into objects and background using FCM algorithm,from which the resulted fuzzy membership of each object is transformed into the initial value of level set function with respect to the LBF model.Starting from the initial value,the evolution of LBF model is continued until convergence.Thus,the segmentation is accomplished.Experimental results on the synthetic and real images(blood vessel images and the brain image) show that the proposed algorithm can get the suitable initial value automatically.As a result,the sensitivity to the initial contour is solved effectively and the iteration number is also decreased considerably.Moreover,the multiple objects segmentation can be implemented by choosing the different cluster generated previously from FCM algorithm.