Multi-modal image registration is a challenging task that is vital to fuse complementary signals for subsequent analyses. Despite much research into cost functions addressing this challenge, there exist cases in which these are ineffective. In this work, we show that (1) this is true for the registration of in-vivo Drosophila brain volumes visualizing genetically encoded calcium indicators to an nc82 atlas and (2) that machine learning based contrast synthesis can yield improvements. More specifically, the number of subjects for which the registration outright failed was greatly reduced (from 40 to 15 ) by using a synthesized image.
@cite_3 simultaneously segmented and registered histological images. The registration step minimizes the mutual information of class labels. This approach is an excellent choice when the modalities have corresponding, but differently appearing pixel classes but may have difficulties in cases where the boundaries of various labels are unclear, or when image content differs significantly.
@cite_3
1966119235
We describe an automatic method for fast registration of images with very different appearances. The images are jointly segmented into a small number of classes, the segmented images are registered, and the process is repeated. The segmentation calculates feature vectors on superpixels and then it finds a softmax classifier maximizing mutual information between class labels in the two images. For speed, the registration considers a sparse set of rectangular neighborhoods on the interfaces between classes. A triangulation is created with spatial regularization handled by pairwise spring-like terms on the edges. The optimal transformation is found globally using loopy belief propagation. Multiresolution helps to improve speed and robustness. Our main application is registering stained histological slices, which are large and differ both in the local and global appearance. We show that our method has comparable accuracy to standard pixel-based registration, while being faster and more general.