Robust Image Segmentation Using Fuzzy C-Means Clustering With Spatial Information Based on Total Generalized Variation

Robust Image Segmentation Using Fuzzy C-Means Clustering With Spatial Information Based on Total Generalized Variation

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Fuzzy c-means clustering (FCM) has proved highly successful in the Collections manipulation and analysis of image information, such as image segmentation.However, the effectiveness of FCM-based technique is limited by its poor robustness to noise and edge-preserving during the segmentation process.To tackle these problems, a new objective function of FCM is developed in this work.The main innovation work and results of this paper are outlined as follows.

First, a regularization operation performed by total generalized variation (TGV) is used to guarantee noise smoothing and detail preserving.Second, a weight factor incorporated into the spatial information term is designed to form nonuniform membership functions, which can contribute to the assignment of each pixel for the highest membership value.In addition, a regularization parameter is used to balance the respective importance of penalty between whole image and each neighborhood.The main advantage of this technique over conventional FCM-based methods is that it can reconstruct image patterns in heavy noise with only a small loss.

We perform experiments on both synthetic and real images.Compared to state-of-the art FCM-based methods, the proposed algorithm exhibits a very good ability BOTTLE OPENER to noise and edge-preserving in image segmentation.