An improved fuzzy clustering image segmentation algorithm combining spatial information

Abstract

In order to improve the ability of fuzzy C-means (FCM) clustering algorithm to suppress noise, an improved fuzzy clustering image segmentation algorithm was proposed. First, multi-dimensional image information was set up using non-local spatial information and local spatial information. Secondly, a priori probability was introduced to ensure that the membership degree before each iteration takes into account the spatial information of the image. Finally, the membership penalty item was added to improve the clustering segmentation effect. Experimental results show that compared with fuzzy local information C-means (FLICM)clustering algorithm, fuzzy C-means with local information and kernel metric(KWFLICM)clustering algorithm, fuzzy C-means with non-local spatial information(FCM_NLS)clustering algorithm, and non-local fuzzy C-means clustering image segmentation algorithm based on particle swarm optimization(PSO-WMNLFCM), the improved algorithm has greatly improved partition coefficient and partition entropy, the partition coefficient increased to 0.957 3, and the partition entropy is reduced to 0.059 6.

Keywords

• image segmentation
• fuzzy c-means
• spatial information
• clustering algorithm
• penalty term