Optimization of the Mainzelliste software for fast privacy-preserving record linkage.

BACKGROUND: Data analysis for biomedical research often requires a record linkage step to identify records from multiple data sources referring to the same person. Due to the lack of unique personal identifiers across these sources, record linkage relies on the similarity of personal data such as first and last names or birth dates. However, the exchange of such identifying data with a third party, as is the case in record linkage, is generally subject to strict privacy requirements. This problem is addressed by privacy-preserving record linkage (PPRL) and pseudonymization services. Mainzelliste is an open-source record linkage and pseudonymization service used to carry out PPRL processes in real-world use cases. METHODS: We evaluate the linkage quality and performance of the linkage process using several real and near-real datasets with different properties w.r.t. size and error-rate of matching records. We conduct a comparison between (plaintext) record linkage and PPRL based on encoded records (Bloom filters). Furthermore, since the Mainzelliste software offers no blocking mechanism, we extend it by phonetic blocking as well as novel blocking schemes based on locality-sensitive hashing (LSH) to improve runtime for both standard and privacy-preserving record linkage. RESULTS: The Mainzelliste achieves high linkage quality for PPRL using field-level Bloom filters due to the use of an error-tolerant matching algorithm that can handle variances in names, in particular missing or transposed name compounds. However, due to the absence of blocking, the runtimes are unacceptable for real use cases with larger datasets. The newly implemented blocking approaches improve runtimes by orders of magnitude while retaining high linkage quality. CONCLUSION: We conduct the first comprehensive evaluation of the record linkage facilities of the Mainzelliste software and extend it with blocking methods to improve its runtime. We observed a very high linkage quality for both plaintext as well as encoded data even in the presence of errors. The provided blocking methods provide order of magnitude improvements regarding runtime performance thus facilitating the use in research projects with large datasets and many participants.

Correlia: an ImageJ plug-in to co-register and visualise multimodal correlative micrographs.

The correlation of different microscopic imaging techniques alongside with microanalytical methods is crucial to better understand biological processes on a subcellular level. For that, micrographs and chemical maps exhibiting both, very different spatial resolution and field-of-view but also a highly multimodal content has to be co-registered. We developed the ImageJ/Fiji plug-in Correlia that provides an environment for handling multimodal correlative microscopy data. Several linear and nonlinear registration methods using either feature or area-based similarity measures can flexibly be cascaded to align and warp 2D microscopy data sets. The registration of data sets containing light- and electron micrographs as well as chemical maps acquired by secondary-ion mass spectroscopy and energy-dispersive X-ray spectroscopy is demonstrated. Correlia is an open-source tool developed particularly for the registration and analysis of highly multimodal 2D correlative microscopy data. LAY DESCRIPTION: If a microscopic object is imaged correlatively by two or more different microscopes the acquired micrographs will have to be overlaid accurately using an image-registration software. In cases of relatively similar image content creating such an overlay is straight-forward but what if the fields-of-view and resolutions of the micrographs differ significantly? What if there are distortions in a micrograph which have to be corrected before creating an overlay? What if furthermore a chemical map shall be overlaid that merely shows regions in which a certain chemical element is present? The rapidly increasing number of applications in correlative microscopy is calling for an easy-to-use and flexible image registration software that can deal with these challenges. Having that in mind, we developed Correlia, an ImageJ/Fiji plug-in that provides an environment for handling multimodal 2D correlative microscopy data-sets. It allows for creating overlays using different registration algorithms that can flexibly be cascaded. In this paper we describe what is happening 'under the hood' and give two example data-sets from microbiology which were registered using Correlia. Correlia is open source software and available from www.ufz.de/correlia - including introductory examples, as the authors would like to encourage other scientists to process their individual correlative microscopy data using Correlia.

Visualization and co-registration of correlative microscopy data with the ImageJ plug-in Correlia.

Correlative microscopy experiments require the co-registration of the image data acquired by different micro-analytical techniques. Major challenges are the potentially very different fields-of-view and resolutions as well as the multi-modality of the data. To provide microscopists with an easy-to-use software for two-dimensional image co-registration we have developed Correlia, an open source software based on ImageJa/Fiji,b which is fully tailored for the registration of multi-modal microscopy data. It can handle data-sets of in principle arbitrary extent and uses classical approaches, i.e., rigid registration tools or B-spline based deformation models for the correction of both, global and local misalignments, such that a fast registration output is provided. Here we describe some of the basics of Correlia focusing on its application: firstly, registration workflows are outlined on artificial data. In the second part these recipes are applied to register correlative data acquired on an algal biofilm and a soil sample.