Landmark selection for shape model construction via equalization of variance

Model-based segmentation approaches, such as those employing active shape models (ASMs), have proved to be useful for medical image segmentation and understanding. To build the model, we need an annotated training set representing correspondences among shapes. Manual positioning of landmarks is a tedious, time consuming, and error prone task, and almost impossible in the 3D space. To overcome some of these drawbacks, we devised an automatic method. Our method is guided by the strategy of equalization of the variance contained in a training set for selecting landmarks. The main premise here is that this strategy itself takes care of the correspondence issue and at the same time deploys landmarks very frugally and optimally considering shape variations. The desired landmarks are positioned around each contour in such a manner as to equally distribute the total variance existing in the training set. The method is evaluated on 40 MRI foot data sets. The results show that, for the same number of landmarks, the proposed method is significantly more compact than manual and equally spaced annotations.