• Copper alloy grain size measurement following ASTM E-112 standard.

      ​Fully automated grain size solution, analyze micrographs off of the microscope or run sample data sets in batch. MIPAR generates a grain size distribution as well as data statistics. Fully integrated reports are generated for easy data sharing

    • Nickel-base superalloy, secondary and tertiary gamma prime precipitates (citation) 

      Fully automated and chemically validated solution.

    • Metal powder particle size and shape characterization

      Fully automated solution for quantifying particle size, roundness, and satellites. Analyze a single scan or batch a sample set.

    • Nanofiber porosity and thickness analysis

      Quantify fiber density, void porosity and fiber thickness distribution.   

    • Crack analysis, identify part defects

      Quantify additive manufacturing crack density, size distribution, localized density. Fully automated solution for quantifying a single field or an entire surface stitch. 
    • ​​Original image by Nephron/Wikimedia Commons. CC BY-SA 3.0.

      Masson's trichrome staining of non-alcoholic fatty liver disease

      Fully automated identification and quantification of fibrosis and stenosis.
    • Original image by Flickr/mamarie. CC BY-SA 2.0.

      ​Cells expressing fluorescently-labeled HSP70 (green) and/or P73α (red).

      Fully automated count of nuclei, with the percentage expressing HSP70, P73α, or both.
    • Original image by Andrew Romanowski

      DIC micrographs of leaf epidermis

      Semi-automated identification of epidermal cells, saving time and increasing accuracy in the face of challenging images

      Fully-automated identification of stomata, powered by deep learning.
    • Original image courtesy of Aadil El-Turabi

      Electron microscopy of viral particles

      Fully automated virus identification, with diameter and shape measurement
    • Original image courtesy of Faith Brennan

      Bacterial colonies on an agar plate

      Automatic plate detection

      Estimated count of colonies which have grown together


    • Automated railtrack inspection via drone imaging

      Railtrack components quantified from drone imagery as part of a weekly railway inspection mandated by the federal railroad administration (FRA)

    • Remote sensing of houses and trees in residential community

      Objects automatically detected and classified into houses and trees, with a count of each class reported.

    • Filter paper contaminant classification and size measurement

      Detected contaminants are classified into predefined types and the size and shape reported.

    • Inventory count of palette types in storage field

      Palettes detected and classified into their different types. Enabled drones to be used for automated inventory management of vast storage fields.
Why MIPAR?

Facing tedious and variable measurements from images, researchers use MIPAR to detect and measure features:

✓   Significantly faster and with much less user variability than hand analysis​
✓   With greater accuracy than other automated solutions​​
​✓   With built-in supervision over your results
How Does MIPAR Compare?