Correlation & spectroscopy analysis software for microscopy

• Colocalize images and spectral maps from a single instrument or multiple instruments (Raman spectroscopy, cathodoluminescence, fluorescence, EDX, EELS, AFM, SEM etc.)
• Combine information from different sources into a single, multimodal dataset

Left: colocalization of EDS maps, SEM image and AFM topography.

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• Process Raman, cathodoluminescence, photoluminescence, fluorescence, EDX/EDS and EELS maps
• Choose which layers of data you wish to use in rendering
• Color mixing tool: choose to mix layer colors or use dominant color
• Reduce image noise
• Play with transparency levels

• Correct and enhance images obtained through spectroscopic techniques
• Apply smoothing filters, remove image artefacts, fix lighting and interactively adjust brightness and contrast.

• Create 3D chemical maps by colocalization with microscopy images or topography data whenever available (TERS, confocal, SEM stereo reconstruction etc.)
• Generate chemical maps with 3D-texture effects using single SEM images or optical microscopy images as backgrounds

• Associate tomography & chemical analysis
• Analyze each voxel as multiple channels, one per material (for example using confocal Raman, FIB-SEM in EDX imaging mode)
• Choose from a wide range of visualization & animation settings including dynamic image segmentation and filtering
• Use color mixing and 3D image segmentation simultaneously to see where particles of one material are located in relation to others in the cube
• Show/hide each material in just one click (and choose to “cut away” any material when you wish to see the others)
• Extract slices as color images
• Cut through the cube in any orthogonal direction to extract a multi-channel image (chemical map) for 2D analysis
• Generate Shell studiables (freeform surfaces) from multi-channel cubes to study particle morphology
• Produce particle and grains statistics directly from cube data

• Get spectra ready for analysis: correct the base-line & filter to remove noise
• Display spectral bands as a colored image
• Reduce data cubes to a number of significant spectra using multivariate analysis