Segmentation of dental radiography allows the identification of human individuals but also could be used for the development of more effective diagnostic, monitoring, and evaluation of appropriate treatment plans. In practice, dark background and bones tissues are not distinguished with contour extraction methods on dental images. So we propose to first apply the k-means method and then extract the contours on the clustering result. We present an initialization of the k centroids based on the grey scale histograms, a weighted norm that includes both grey scale and geometrical information, and tests it on dental X-ray images. Then we describe a promising parallel clustering method based on kernel affinity.