Biomedical image registration faces challenging problems induced by the image acquisition process of the involved modality. A common problem is the omnipresence of noise perturbations. A low signal-to-noise ratio - like in modern dynamic imaging with short acquisition times - may lead to failure or artifacts in standard image registration techniques. A common approach to deal with noise in registration is image presmoothing, which may however result in bias or loss of information. A more reasonable alternative is to directly incorporate statistical noise models into image registration. In this work we present a general framework for registration of noise perturbed images based on maximum a-posteriori estimation. This leads to variational registration inference problems with data fidelities adapted to the noise characteristics, and yields a significant improvement in robustness under noise impact and parameter choices. Using synthetic data and a popular software phantom we compare the proposed model to conventional methods recently used in biomedical imaging and discuss its potential advantages. © 2014 Springer International Publishing.
CITATION STYLE
Suhr, S., Tenbrinck, D., Burger, M., & Modersitzki, J. (2014). Registration of noisy images via maximum a-posteriori estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8545 LNCS, pp. 231–240). Springer Verlag. https://doi.org/10.1007/978-3-319-08554-8_24
Mendeley helps you to discover research relevant for your work.