In this paper we present a system for segmenting medical images using statistical shape models (SSM's) which is landmark free, fully 3D, and accurate. To overcome the limitations associated with previous 3D landmark-based SSM's, our system creates a levelset-based SSM which uses the minimum distance from each voxel in the image to the object's surface to define a shape. Subsequently, an advanced statistical appearance model (SAM) is generated to model the object of interest. This SAM is based on a series of statistical texture features calculated from each image, modeled by a Gaussian Mixture Model. In order to segment the object of interest on a new image, a Bayesian classifier is first employed to pre-classify the image voxels as belonging to the foreground object of interest or the background. The result of the Bayesian classifier is then employed for optimally fitting the SSM so there is maximum agreement between the SAM and the SSM. The SAM is then able to adaptively learn the statistics of the textures of the foreground and background voxels on the new image. The fitting of the SSM, and the adaptive updating of the SAM is repeated until convergence. We have tested our system on 36 T2-w, 3.0 Tesla, in vivo, endorectal prostate images. The results showed that our system achieves a Dice similarity coefficient of.84±.04, with a median Dice value of.86, which is comparable (and in most cases superior) to other state of the art prostate segmentation systems. Further, unlike most other state of the art prostate segmentation schemes, our scheme is fully automated requiring no user intervention.