Speaker
Description
Università degli Studi di Perugia
CURI
Multimodal image registration plays an important role in biomedical imaging, since it enables the integration of complementary information from different techniques, including Mass Spectrometry Imaging (MSI), fluorescence imaging, and optical imaging. The combination of molecular and morphological data can improve tissue characterization and support diagnostic and therapeutic applications.
The coregistration of these datasets is intrinsically challenging due to differences in spatial resolution, contrast, noise, and anatomical deformation. As a result, robust alignment often requires preliminary preprocessing and image reduction steps.
In collaboration with the Umbrian Center for Research and Innovation (CURI), this work is focused on the development and validation of an algorithmic pipeline for multimodal biomedical image coregistration, aimed at the integration of heterogeneous imaging data within an applied research framework.