Fuzzy clustering using local and global region information for cell image segmentation

In high-throughput applications, accurate segmentation of biomedical images can be considered as an important step for recognizing cells that have the phenotype of interest. In this paper, while conventional fuzzy clustering is not able to implement the local and global spatial information, a novel spatial fuzzy clustering cell image segmentation algorithm is proposed. The segmentation procedure is divided into two stages: the first stage involves processing the local and global spatial information of the given cell image and a final segmentation stage which is based on the idea of conventional fuzzy clustering. Our idea can be considered as a sequential integration of region based methods and fuzzy clustering for cell image segmentation. Experimental results show that the proposed model yields significantly better performance in comparison with several existing methods.