Automatic Landmark Detection Using Statistical Shape Modelling and Template Matching

We propose a new methodology for automated landmark detection for breast MR images that combines statistical shape modelling and template matching into a single framework. The method trains a statistical shape model (SSM) of breast skin surface using 30 manually labelled landmarks, followed by generation of template patches for each landmark. Template patches are matched across the unseen image to produce correlation maps. Correlation maps of the landmarks and the shape model are used to generate a first estimate of the landmarks referred to as “shape predicted landmarks”. These landmarks are refined using local maximum search in individual landmarks correlation maps. The algorithm was validated on 30 MR images using a leave-one-out approach. The results reveal that the method is robust and capable of localising landmarks with an error of 3.41 ± 2.10 mm.