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A Modular Suite of Advanced Bioimaging Tools with scikit-image and Dash
To bring the combined power of scikit-image and Dash to a larger number of scientists thanks to increased execution speed, interactive image annotation and processing, and outstanding documentation targeting life sciences practitioners.
scikit-image is the open-source image processing toolkit of Scientific Python. It proposes a collection of algorithms which address the various image processing tasks encountered in science (denoising, segmentation, feature extraction…). scikit-image is application-agnostic and its algorithms accept both two-dimensional and three-dimensional (sometimes n-d) images for compatibility with the various image modalities (microscopy, tomography, MRI, etc.) of science. However, it is a core dependence of many application-specific image processing packages such as CellProfiler or hyperspy. scikit-image targets a wide community of students, engineers and scientists, many of them self-taught about image processing. An extensive documentation is found on the project website, including some narrative documentation and “getting started” instructions, and also a popular gallery of examples illustrating the use of scikit-image algorithms.