Abstract
Fuzzy C-means (FCM) clustering is an unsupervised clustering technique and is often used for the unsupervised segmentation of multivariate images. The segmentation of the image in meaningful regions with FCM is based on spectral information only. The geometrical relationship between neighboring pixels is not used. In this paper, a semi-supervised FCM technique is used to add geometrical information during clustering. The local neighborhood of each pixel determines the condition of each pixel, which guides the clustering process. Segmentation experiments with the Geometrically Guided FCM (GG-FCM) show improved segmentation above traditional FCM such as regions that are more homogeneous and less spurious pixels.