Image Segmentation with Anisotropic Weighted Fuzzy C-Means Clustering

Since it does not take into account the image characteristics as well as the correlation of neighbor pixels,the standard fuzzy C-means(FCM) is very sensitive to noise.Although some improved methods do possess the anti-noise property,their resultant edges of segmentation may be blurred due to the use of low-pass filters,such as the averaged filter.To overcome these drawbacks,we propose a new FCM based image segmentation method where an anisotropic weight is assigned to each pixel in the neighborhood.In addition,a fast anisotropic weighted fuzzy C-means clustering algorithm is also proposed.The experimental results show that our method has the stronger anti-noise property,better robustness to various noises and higher segmentation accuracy.