Multiscale and Multimodal Image Fusion. Coping with Differences in Scanned Area and Spatial Resolution for Raman/Fluorescence Images of Labeled Cells

Multiscale and multimodal image fusion is a challenge derived from the diversity of chemical and spatial information provided by the current hyperspectral image platforms. Efficient image fusion approaches are essential to exploit the complementary chemical information across different zoom scales. Most current image fusion algorithms tend to work by equalizing the spatial characteristics of the platforms to be combined, i.e., downsampling pixel size and cropping noncommon scanned sample areas if required. In this work, a new image unmixing algorithm based on a flexible mathematical framework is proposed to enable working with all available image information while preserving the original spatial properties of every imaging measurement. The algorithm is tested on a challenging image fusion scenario of fluorescence and Raman images collected on labeled HeLa cells. The system is relevant from an analytical point of view, since smart fluorescence labeling allows profiting from the excellent morphological information without causing interferences in the rich chemical information furnished by Raman. From a data handling perspective, it offers a challenging multiscale problem, where the fast fluorescence imaging acquisition allows recording full cell images, and the slower Raman image acquisition is focused on scanning only relevant small regions of the cells analyzed. By applying the image fusion algorithm proposed, an improved morphological and chemical characterization of cell constituents in the full cell area is obtained despite the different spatial scales used in the original imaging measurements.