In biological microscopy, the ever expanding range of applications requires quantitative approaches that analyze several distinct fluorescent molecules at the same time in the same sample. However, the spectral properties of the fluorescent proteins and dyes presently available set an upper limit to the number of molecules that can be detected simultaneously with common microscopy methods. Spectral imaging and linear unmixing extends the possibilities to discriminate distinct fluorophores with highly overlapping emission spectra and thus the possibilities of multicolor imaging.
Detects statistically robust phenotypic differences. MiCASA is an unbiased, user-independent method for identifying the phenotypic differences between conditions. It focuses attention on those aspects of the phenotype that have statistical relevance. This package may be used to provide a useful summary of the distribution and interactions between well-known cell types in the thymus. However, MiCASA can be used with any pair of markers.
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 preprocessing and subsequent multivariate analysis of spectral imaging data. SpectralAnalysis is an analysis software that can be used through the entire analysis workflow, for data sets acquired from single experiments to large multi-instrument, multimodality, and multicenter studies. The software enables testing of algorithms with instant visualization of the results. It is suitable for comparisons of preprocessing methods on mass spectrometry imaging (MSI) data acquired on any instrument.
A software application on MATLAB to visualize surface data from 3D fluorescence microscopy. Map3-2D accurately accurately projects up to five-dimensional (5D) fluorescence microscopy image data onto full-content 2D maps. Similar to the Earth's projection onto cartographic maps, Map3-2D unfolds surface information from a stack of images onto a single, structurally interconnected map. By cross-referencing between the 2D map and the original image stack, precise quantitative analyses of intensities and shapes are easily and intuitively executable.
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.
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