In this work, we present a framework for multi-modal deformable registration of immunofluorescence to histology images. It is cast to a mono-modal registration problem by iteratively propagating tissue-specific features from one modality to generate intensities in the other modality via an implicitly learnt random-forest regression framework. The proposed method iterates between modality propagation and image registration in a unified formulation. Evaluations on real and simulated tissue deformations establish superiority of the proposed work over comparative methods in handling highly complex intermodal intensity relationships. This framework will aid in quantitative analysis of the tissue structure from histology with correlated with functional information from immunofluorescence.     «

In this work, we present a framework for multi-modal deformable registration of immunofluorescence to histology images. It is cast to a mono-modal registration problem by iteratively propagating tissue-specific features from one modality to generate intensities in the other modality via an implicitly learnt random-forest regression framework. The proposed method iterates between modality propagation and image registration in a unified formulation. Evaluations on real and simulated tissue deforma...     »