A method of approximating the boundary of an object in an image, the image being represented by a data set, the data set comprising a plurality of data elements, each data element having a data value corresponding to a feature of the image, the method comprising determining which one of a plurality of contours most closely matches the object boundary at least partially according to a divergence value for each contour, the divergence value being selected from the group consisting of Jensen-Shannon divergence and Jensen-Renyi divergence. Each contour C.sup.i defines a zone Z.sub.I.sup.i and a zone Z.sub.O.sup.i, Z.sub.I.sup.i representing the data elements inside the contour and Z.sub.O.sup.i representing the data elements outside the contour, each zone having a corresponding probability distribution of data values for the data elements therein, and wherein the divergence value for each contour C.sup.i represents a measure of the difference between the probability distributions for the zones Z.sub.I.sup.i and Z.sub.O.sup.i. The boundary estimate is preferably a parametric contour. Further, the present invention supports the segmentation of multiple objects in a single data set simultaneously.