17. A computer readable medium encoded with computer executable instructions for detecting a plurality of anatomical landmarks in an image, the computer executable instructions defining steps comprising: detecting a first landmark of the plurality of anatomical landmarks in the image using marginal space learning (MSL); estimating locations of remaining landmarks of the plurality of anatomical landmarks in the image based on the detected first landmark using a learned geometric model relating the plurality of anatomical landmarks; and detecting each of said remaining landmarks using MSL in a portion of the image constrained based on the estimated location of each of said remaining landmarks.
17. A computer readable medium encoded with computer executable instructions for detecting a plurality of anatomical landmarks in an image, the computer executable instructions defining steps comprising: detecting a first landmark of the plurality of anatomical landmarks in the image using marginal space learning (MSL); estimating locations of remaining landmarks of the plurality of anatomical landmarks in the image based on the detected first landmark using a learned geometric model relating the plurality of anatomical landmarks; and detecting each of said remaining landmarks using MSL in a portion of the image constrained based on the estimated location of each of said remaining landmarks. 21. The computer readable medium of claim 17 , wherein the computer executable instructions defining the step of detecting each of said remaining landmarks using MSL in a portion of the image constrained based on the estimated location of each of said remaining landmarks comprise computer executable instructions defining the steps of, for each of said remaining landmarks: detecting position candidates in a portion of the image within a certain distance of the estimated location of the landmark using a trained position classifier; generating position-orientation hypotheses from said position candidates; detecting position-orientation candidates from said position-orientation hypotheses using a trained position-orientation classifier; generating similarity transformation hypotheses from said position-orientation candidates; and detecting at least one similarity transformation candidate from said similarity transformation hypotheses using a trained similarity transformation classifier.