Learning Correspondences in Knee MR Images from the Osteoarthritis Initiative - Guerrero, Ricardo; Donoghue, Claire R.; Pizarro, Luis; Rueckert, Daniel (Machine Learning in Medical Imaging, 2012)
Registration is a powerful tool that allows mapping images in a common space in order to aid in their analysis. Accurate registration of images of the knee is challenging to achieve using intensity based registration algorithms. Problems arise due to large anatomical inter-subject differences which causes registrations to fail to converge to an accurate solution. In this work we propose learning correspondences in pairs of images to match self-similarity features, that describe images in terms of their local structure rather than their intensity. We use RANSAC as a robust model estimator. We show a substantial improvement in terms of mean error and standard deviation of 2.13mm and 2.47mm over intensity based registration methods, when comparing landmark alignment error.