Cross-modal registration using point clouds and graph-matching in the context of correlative microscopies
This work provides a method for biologists to perform registration in correlative microscopy without introducing bias, by allowing them to select image content for point cloud generation.
This paper addresses the challenge of registering point clouds with significant density differences, missing parts, and outliers, which are generated from selected content by biologists in correlative microscopy workflows. The authors propose a graph-matching based method for point cloud registration and compare its performance against iterative closest point (ICP) based methods.
Correlative microscopy aims at combining two or more modalities to gain more information than the one provided by one modality on the same biological structure. Registration is needed at different steps of correlative microscopies workflows. Biologists want to select the image content used for registration not to introduce bias in the correlation of unknown structures. Intensity-based methods might not allow this selection and might be too slow when the images are very large. We propose an approach based on point clouds created from selected content by the biologist. These point clouds may be prone to big differences in densities but also missing parts and outliers. In this paper we present a method of registration for point clouds based on graph building and graph matching, and compare the method to iterative closest point based methods.