A limited memory algorithm for bound constrained optimization
SIAM Journal on Scientific Computing
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Optimization of mutual information for multiresolution image registration
IEEE Transactions on Image Processing
Unifying Encoding of Spatial Information in Mutual Information for Nonrigid Registration
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Symmetric deformable image registration via optimization of information theoretic measures
Image and Vision Computing
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Nonrigid registration of multitemporal CT and MR images for radiotherapy treatment planning
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
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Because of its robustness and accuracy for a variety of applications, either monomodal or multimodal, mutual information (MI) is a very popular similarity measure for (medical) image registration. Calculation of MI is based on the joint histogram of the two images to be registered, expressing the statistical relationship between image intensities at corresponding positions. However, the calculation of the joint histogram is not straightforward. The discrete nature of digital images, sampled as well in the intensity as in the spatial domain, impedes the exact calculation of the joint histogram. Moreover, during registration often an intensity will be sought at a non grid position of the floating image. This article compares the robustness and accuracy of two common histogram estimators in the context of nonrigid multiresolution medical image registration: a Parzen window intensity interpolator (IIP) and generalised partial volume histogram estimation (GPV). Starting from the BrainWeb data and realistic deformation fields obtained from patient images, the experiments show that GPV is more robust, while IIP is more accurate. Using a combined approach, an average registration error of 0.12 mm for intramodal and 0.30 mm for intermodal registration is achieved.