Spectral unmixing is the procedure by which the measured spectrum of a mixed pixel is decomposed into a collection of constituent spectra, or endmembers, and a set of corresponding fractions, or abundances, that indicate the proportion of each endmember present in the pixel. Endmembers normally correspond to familiar macroscopic objects in the scene, such as water, soil, metal, or any natural or man-made material. Unmixing provides a capability that is important in numerous tactical scenarios in which subpixel detail is valuable.
It is an organized collection of software modules for image data handling, pre-processing, segmentation, inspection and editing, post-processing, and secondary analysis. These modules can be scripted to accomplish a variety of automated image analysis tasks.
Allows users to measure and correct spectral bleedthrough between different color channels from reference images. Spectral Unmixing Plugins is an algorithm that can be applied to fluorescence images. With this tool, the relative intensity of each fluorochrome in all color channels is stored in a matrix called the “mixing matrix”.
Detects and separates spectrally distinct components of multiply labeled fluorescence images. PoissonNMF is an algorithm adapted to fluorescence microscopy that can be used as an analytical tool. This method can be applied for laser-scanning and wide-field microscopes that provide spectrally resolved data. It operates on spectrally resolved images and delivers both the emission spectra of the identified components and images of their abundance.
Provides a high-throughput image processing workflow designed for reducing hands-on analysis time confocal, epi-fluorescence, and two-photon microscopy images. Chrysalis can automate image processing steps. This method identifies subtle differences in cell phenotype and cell-cell interactions, while also offering significant reduction in hands-on analysis time. It can be applied to a broad range of biological questions.
Augments the constrained least squares (CLS) method by a morphological constraint to enable morphologically constrained spectral analysis. The Unmixing_MCSU overcomes the fundamental challenge of separating fluorophores with very similar emission spectra by exploiting spatial cues that are often available in multi-spectral microscopy data. It also provides an option to extract the true spectra from the data itself.
Allows users to study images contaminated with both autofluorescence and background fluorescence. SSASU consists of an algorithm able to reduce the impact of autofluorescence and background and unmix seven fluorophores. Additionally, it can separate fluorescence end-members in the presence of autofluorescence and background fluorescence.