Geometrically guided fuzzy C-means clustering for multivariate image segmentation

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 neighbouring pixels is not used. In this paper, a semi-supervised FCM technique is used to add geometrical information during clustering. The local neighbourhood 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 more homogeneous regions and less spurious pixels.