A method for segmenting an object of interest from an image of a patient having such object. Each one of a plurality of training shapes is distorted to overlay a reference shape with a parameter .THETA..sub.i being a measure of the amount of distortion required to effect the overlay. A vector of the parameters .THETA..sub.i is obtained for every one of the training shapes through the minimization of a cost function along with an estimate of uncertainty for every one of the obtained vectors of parameters .THETA..sub.i, such uncertainty being quantified as a covariance matrix .SIGMA..sub.i. A statistical model represented as {circumflex over (f)}.sub.H (.THETA.,.SIGMA.) is generated with the sum of kernels having a mean .THETA..sub.i and covariance .SIGMA..sub.i. The desired object of interest in the image of the patient is identified by positioning of the reference shape on the image and distorting the reference shape to overlay the obtained image with a parameter .THETA. being a measure of the amount of distortion required to effect the overlay. An uncertainty is quantified as a covariance matrix .SIGMA. and an energy function E=E.sub.shape+E.sub.image is computed to obtain the probability of the current shape in the statistical shape model E.sub.shape(.THETA.,.SIGMA.)=-log({circumflex over (f)}.sub.H) and the fit in the image E.sub.image.