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.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, additional information extracted by image analysis methods like organ segmentations, landmarks, local image features etc. will be integrated into a probabilistic, hierarchical registration framework. The registration framework uses an approach based on correspondence probabilities to align images. The methods to develop will enable the simultaneous registration and detection of areas with missing local correspondences as well as the objective assessment of the accuracy and 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.
|Effective start/end date||01.01.08 → 31.12.14|
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
Research Areas and Centers
- Academic Focus: Biomedical Engineering
DFG Research Classification Scheme
- 205-01 Epidemiology, Medical Biometrics, Medical Informatics