2020/12/01 by Stephan Kunne, Guillaume Potier, Kunne, Stephan +5
Biochemistry, Genetics and Molecular Biology · Computer Science · #05C60 #Computer Vision and Pattern Recognition (cs.CV) #FOS: Biological sciences #FOS: Computer and information sciences #I.4 #J.3 #Tissues and Organs (q-bio.TO) #acm:05C60 #cs.CV #msc:05C60 #q-bio.TO
paper · pdf · doi:10.48550/arxiv.2012.00656
in Proceedings of iTWIST'20, Paper-ID: 22, Nantes, France, December, 2-4, 2020
arxiv created 2020/12/01 · arxiv updated 2020/12/02
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.