Severins, I.; Joo, C.; van Noort, J.

Implementation of combined microscopy methods provides valuable information across various scientific applications. However, aligning the datasets and finding the correct point correspondence poses a challenge, especially for large, randomly distributed point sets that are subject to positional errors and missing points. Here, we provide a three-step procedure to perform point set registration, which can be applied to datasets with millions of points and stays robust even when only 10% of the points correspond. In the first global step, the scaling and rotation parameters for the imaging systems are determined once on a smaller calibration dataset using a geometric hashing algorithm. When the global transformation is known, full experimental datasets can be registered by performing step two: a course registration using cross-correlation, and step three: a precise registration to fine-tune the transformation. After these three steps, point correspondence is determined by setting a distance threshold based on a statistical model of random point sets that additionally provides the matching error. We have demonstrated its successful implementation in coupling fluorescence and sequencing methodologies. To enable wide application of these point set registration and correspondence algorithms we provide a python library called MatchPoint.