Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions

We present an adaptive fuzzy clustering scheme for image segmentation, the adaptive fuzzy clustering/segmentation (AFCS) algorithm. In AFCS, the nonstationary nature of images is taken into account by modifying the prototype vectors as functions of the sample location in the image. The inherent high interpixel correlation is modeled using neighborhood information. A multiresolution model is utilized for estimating the spatially varying prototype vectors for different window sizes. The fuzzy segmentations at different resolutions are combined using a data fusion process in order to compute the final fuzzy partition matrix. The results provide segmentations, having lower fuzzy entropy when compared to the possibilistic C-means algorithm, while maintaining the image's main characteristics. In addition, due to the neighborhood model, the effects of noise in the form of single pixel regions are minimized.