Anatomical landmarks such as the anterior commissure (AC) and posterior commissure (PC) are commonly used by
researchers for co-registration of images. In this paper, we present a novel, automated approach for landmark detection
that combines morphometric constraining and statistical shape models to provide accurate estimation of landmark points.
This method is made robust to large rotations in initial head orientation by extracting extra information of the eye centers
using a radial Hough transform and exploiting the centroid of head mass (CM) using a novel estimation approach. To
evaluate the effectiveness of this method, the algorithm is trained on a set of 20 images with manually selected
landmarks, and a test dataset is used to compare the automatically detected against the manually detected landmark
locations of the AC, PC, midbrain-pons junction (MPJ), and fourth ventricle notch (VN4). The results show that the
proposed method is accurate as the average error between the automatically and manually labeled landmark points is less
than 1 mm. Also, the algorithm is highly robust as it was successfully run on a large dataset that included different kinds
of images with various orientation, spacing, and origin.