1. A method of segmenting an object in a set of image data using one or more prior instances of the object, comprising: determining a nonparametric estimate of a statistical shape distribution from the one or more prior instances of the object in a subspace spanned by the one or more prior instances of the object by a kernel density estimator; determining a nonparametric estimate of a statistical intensity distribution from one or more prior instances of the object by a kernel density estimator; combining the kernel density estimator of the statistical shape distribution with the kernel density estimator of the statistical intensity distribution in a Bayesian expression conditioned on the set of image data, wherein the expression is provided in accordance with: E ⁡ ( α , h , θ ) = ⁢ - ∫ Ω ⁢ ( H ϕ ⁢ log ⁢ ⁢ p in ⁡ ( I ) + ( 1 - H ϕ ) ⁢ log ⁢ ⁢ p out ⁡ ( I ) ) ⁢ ⁢ ⅆ x - log ( 1 N ⁢ ⁢ σ ⁢ ∑ i = 1 N ⁢ ⁢ K ( α - α i σ ) ) ; selecting a segmentation of the object in the set of image data by executing a level set method by a processor which optimizes the Bayesian expression; and the processor generating an image of the segmented object on a display.
8. The method as claimed in claim 1 , wherein the set of image data is CT image data.