Integrated Analysis and Probabilistic Registration of Medical Images with Missing Correspondences

The automatic, robust and reliable registration of medical images is a central problem in medical image computing with high impact on image-guided diagnostics and therapy. Currently available registration methods reach their limits, if strong anatomical or pathologic discrepancies are present in the images and corresponding structures are missing in parts of the images. Another limitation of current registration methods is the lack of information they provide to the user about the local (un)certainty of the estimated transformation and therefore does not allow an assessment of the registration results.

The aim of this project is to enable the robust and reliable registration of images even if one-to-one correspondences are missing in parts of the images. To achieve this, a general probabilistic registration framework based on correspondence probabilities is developed that does not only rely on image intensities but also on additional information extracted by image analysis methods like organ segmentations, landmarks and local image features to align images. The methods to develop will enable the registration of areas with missing local correspondences as well as the objective assessment of the reliability of the local registration results.

The proposed methodical innovations extend the medical application spectrum of image registration algorithms, significantly. For example, the proposed method will facilitate and improve the quality of image-based follow-up studies and clinical monitoring, comparison of pre- and post-operative images as well as image-based statistical studies to reveal spatial distribution patterns of pathological tissues or neuronal activities.

Project Team

M.Sc. Sandra Schultz
Dr. rer. nat. Jan Ehrhardt
Prof. Dr. rer. nat. habil. Heinz Handels