New FCM Image Segmentation Based on Texture Measure and Adaptive Threshold

Image segmentation is a classic inverse problem which consists of achieving a compact region-baseddescription of the image scene by decomposing it into meaningful or spatially coherent regions sharing similarattributes.Fuzzy C-means(FCM)clustering is one of well-known unsupervised clustering techniques,which hasbeen widely used in automated image segmentation.However,when the FCM algorithm is used for imagesegmentation,there are also some problems,such as poor robustness against noise,slow segmentation speed etc.In this paper,we present a novel FCM image segmentation based on texture measure and adaptive threshold.Firstly,the image feature is extracted according to Laws texture measure,and the initial segmentation isperformed on origin image by using FCM algorithm.Then,the adaptive thresholds are computed by utilizing theOtsu rule and FCM algorithm,and a region combination is performed on the initial segmentation image.Experimental results showed the proposed method achieves competitive segmentation results compared to otherFCM-based methods,and is in general faster.