Data-specific feature point descriptor matching using dictionary learning and graphical models

The identification of anatomical landmarks in medical images is an important task in registration and morphometry. The manual identification and labeling of these landmarks is very time consuming and prone to observer errors, especially when large datasets must be analyzed. Matching landmarks in a pair of images is a challenging task. Although off-the- shelf feature point descriptors are powerful at describing points in an image, they are generic by nature, as they have been usually developed for applications in a computer vision setting where there is little prior knowledge about the images. Leveraging on recent developments in the machine learning community, this paper aims to build feature point descriptors that are dataset-specific. The proposed approach describes landmarks as feature descriptors based on a sparse coding reconstruction of a patch surrounding the landmark (or any point of interest), using a dataset-specific learned dictionary. Since strong spatial constraints typically exist in medical images, we also combine spatial information of surrounding point descriptors into a graphical model that is built online. We show accurate results in matching one-to-one anatomical landmarks in brain MR images.