Quantitative Localisation of Manually Defined Landmarks

In this paper we propose a method for automatic placement of landmark points in 3D volumes, for use in morphological studies, which addresses the issue of quantitative estimation of measurement error. We are given a sample 3D volume of image slices (and a number of manually annotated landmark points) and develop an algorithm that is capable of estimating landmark locations in a similar but novel 3D volume. Problems are found which arise from the local minima generated when constructing likelihood functions using shifted noisy image patches, which must be addressed during both localisation and covariance estimation. The method is tested using Monte-Carlo in order to evaluate the quantitative validity of error estimates.