Image segmentation using spatial fuzzy C means clustering and grey wolf optimizer

Image segmentation is one research area of image processing which has many applications in practice. In this paper we have undertaken image segmentation problem using spatial fuzzy c means (SFCM) clustering which is an unsupervised classification scheme. A good segmentation result is desirable for classification problem especially in medical image classification. Therefore SFCM clustering result is further optimized using a new colony based optimization algorithm called the grey wolf optimizer (GWO). Experiments were conducted for segmentation of magnetic resonance imaging (MRI) images. Comparative results are presented to show the effectiveness of incorporating GWO in to SFCM for image segmentation problem. Performance parameters are given in terms of clustering validity functions.