This paper presents a generic and unified method to identify a set of anatomical landmarks of interest within the medical image domains. Landmark identification is important as it provides us with: 1) initial information for registration, 2) navigation and retrieval guidance through the image data, 3) initial models for segmentation, and 4) valuable (though rough) information about the organs/structures of interest. The proposed method initially uses a supervised learning procedure and then improves itself based on the Bayes’ theory. The procedure at the first step requires an expert to define a rough roadmap passing through a set of high-contrast landmarks (milestones), and eventually reaching at the structure of interest. The expert is asked to mark the milestones as desired points and a few points around them as undesired points, respectively. Then we estimate Gaussian models for the marked points by which the optimal search area for each desired landmark is determined. The search areas estimated at this step are considered as the segments of the statistical roadmap. An additional set of statistical models along with the above ones are used to form a set of rules to evaluate the points being found during the search procedure. The points that satisfy the rules will be recognized as the landmarks of interest. As the above method is being applied on a set of new patients/cases, a set of valid landmarks of interests becomes available. This new piece of information is then being used to modify the current statistical roadmap based on the Bayes' theory. We have applied the proposed method on T1-weighted brain MRI of 10 epileptic patients to find the landmarks of the hippocampus. In our experiment, six patients formed the training set, and we observed one-step iteration of the Bayesian modification. The method made no false alarms. The overall success rate (average of sensitivity and specificity) of the algorithm was 83.3% with an accuracy of 99.2%. In localizing the hippocampus, the proposed method (with almost perfect results) was 600 times faster than the mutual information registration (with poor and partly wrong results).