Dragonfly is a software platform for the intuitive inspection of multi-scale multi-modality image data. Its user-friendly experience translates into powerful quantitative findings with high-impact visuals, driven by nuanced easy-to-learn controls.
Set apart by Dragonfly's pioneering Deep Learning solution, Dragonfly's segmentation toolkit includes watershed and superpixel methods, 2D histographic segmentation, and other innovative 2D and 3D tools to label features of interest with ease and precision. Dragonfly's quantification tools then provide powerful options for counting, measuring, and characterizing image features, such as pores, fibers, grains, and more.
For high-definition exploration into the details and properties of 3D datasets, Dragonfly delivers sophisticated but simple controls for color, transparency, shadowing, and focus. Interactive inspection with color and opacity mapping means that in-depth analyses can always be visualized in meaningful ways and findings can be presented with easy-to-produce high-quality animated sequences.
More than just an out-of-the-box solution, Dragonfly can be customized through scripting in Python and extended by developing custom add-ons that implement specific tasks and workflows. Execute commands and explore possibilities in Dragonfly's integrated Python console or record and save a sequence of steps in a macro file, which provides a convenient way to automate repetitive tasks.
Our product team can't think of everything. They don't have to. Thanks to an integrated Anaconda Python distribution, Dragonfly users can execute numpy and scipy functions directly in the application, deploy Python scripts that access Dragonfly data objects, and build their own plug-ins by leveraging the powerful image processing algorithms freely available in the Python ecosystem.
Dragonfly's ready-to-use Anaconda distribution for Dragonfly, which includes the best open-source computational packages for image processing, is based on Python 3.7 and includes numpy and scipy. Numpy is an extension for handling multi-dimensional arrays, while scipy provides tools for data preparation, image analysis, segmentation, and machine learning.
With auto-completion for known attributes and functions, Dragonfly's comprehensive Python console allows for easy execution of workflow steps so that you can take the software exactly where you need it to be.
Enhance your understanding of 3D scientific, industrial, and biomedical data acquired from almost any source, including microCT, X-ray microscopy, FIB-SEM and TEM systems, and other modalities.
Dragonfly provides the ideal framework for specialized workflows across a wide range of application areas with advanced post-processing capabilities that include 2D and 3D image processing, slice registration, object separation, as well as feature extraction and measurement tools.
Dragonfly refines the analysis and characterization of material samples to help researchers and engineers thoroughly understand the structures and physical properties of different materials and turn insights into innovation and improve product quality.
Dragonfly's advanced 3D imaging workflows offer capabilities for exploration and experimentation at multiple scales and dimensions for understanding the structures and properties of metals, composites, ceramics, polymers, fibrous materials and porous media, as well as batteries, fuel cells, and other energy materials.
Dragonfly facilitates the intuitive analysis of multi-modality biological and biomedical image data by letting researchers design and adapt segmentation and detection workflows to experimental setups in such domains as cellular biology, neuroscience, zoology and plant sciences, and bioengineering.
NOTE Dragonfly is NOT a clinically approved medical device.
Leverage the full value of your mineralogy and petrology data and make detailed observations and characterization decisions with Dragonfly workflows that accelerate your understanding of reservoir and source rock samples, petrophysical data, and the properties of raw materials.
Dragonfly helps solve technological problems related to shrinking geometries, new materials, and increasingly complex structures and minimize time-to-result using workflows to gain an understanding of structure-property relationships, package construction, and reliability.
For manufacturing and industrial applications, including electronics, automotive, chip packaging, and additive manufacturing, Dragonfly provides wall thickness, inclusion analysis, pore quantification, and other analysis routines to investigate properties, perform failure analysis, and characterize process efficiency.