2D IMAGE ANALYSIS SOFTWARE
Capture, process, count, classify, measure and share.
A powerful and customizable imaging platform driven by over 30 years of user feedback, the new Image-Pro Premier offers native 64-bit support, a user friendly interface and a suite of 2D measurement solutions.
Rely on our worldwide network of Image-Pro partners, superb training tools, and technical support team to help you get started and keep your research moving forward.
SEGMENT, COUNT/SIZE, CLASSIFY, and ANALYZE
Image-Pro’s technique for performing Automatic Measurements is the foremost solution for gathering data from images by segmentation systems. Our simple step-wise approach to the problem is designed to provide the ultimate flexibility to analyze nearly any image type while remaining simple enough to quickly learn and teach to others.
Identify what you want to measure
Use the method that best suites the image and identify what you want to measure
Select a single intensity rangeUse histogram-based methods to select your areas of interest, instantly highlighting them as objects on the image. For high contrast images use the Auto Dark and Auto Bright methods.
Select multiple intensity ranges (classes)Don’t stop at a single range, add any number of classes to segment any range of intensities, whether contiguous or spaced apart on the histogram, classes can be a great way to find various materials in an image.
Select with the pickerThis simple, but handy, tool enables you to use a range of sizes for the picking area under the cursor and select those pixels as the representative selection on the histogram for your segmentation.
A revolutionary new method that employs a pixel classification algorithm able to identify hard to segment objects and regions. Use Smart Segmentation to identify faintly-colored objects, textured objects, and objects or regions on uneven backgrounds.
Select objects and backgroundEasily add object markers to the image to identify representative samples of your objects or regions and lastly add a background marker. Set up multiple object classes and even create select multiple regions per class to refine the representative pixels.
Define the recipe parametersUse a range of “recipe” options to segment, including Intensity, Color, Background and Morphological Filters. Once you have a recipe that fits your segmentation needs, you can save and reapply to other images. For more complex samples a custom option can pre-apply filters.
Train over multiple imagesMost experiments require the acquisition of many images from one or more samples. Smart Segmentation makes this analysis easy because the algorithm can be trained over a range of images in order to learn how your objects should be most accurately segmented.
Smart Segmentation solves common problems.
Automatically compensate for uneven backgrounds
Select two regions, and Smart Segmentation will automatically calculate the difference in the unevenly illuminated background. This is normally very difficult with histogram-based methods as objects will typically be the same intensity as some areas of the background.
Select & Classify Objects by Color
Locate and segment objects based on their color. Create new classes to further characterize and streamline data collection and reporting. Creating classes is easy, and doesn’t have a limit to how many you can create, in order to make it easy to organize important objects.
Create object outlines, instantly counted and sized
Filter by Measurement
Choose from a wide number of measurements to filter the entire segmentation group from and apply specific range restrictions (graphically or numerically) that selectively leave your objects of interest for further classification and measurement.
Area = ( 486 – Max )
Objects in range = 210
Filter out objects to be counted and sized by any number and combinations of parameters.
Area = ( 1486 – Max )
Objects in range = 57
Reduce the segmented objects down to the proper number and then press COUNT/SIZE.
Create object outlines.
Once objects are identified and outlined you can count and measure areas, percent area, regions, intensity values, and more.
All discrete, separate objects can be automatically counted within the set range of intensities.
Size and Shape
Automatically measure object area, percent area, perimeter, length & width, radii and feret ratio, etc.
Intensity and Density
Automatically measure object intensity, integrated optical density, density, and intensity over time.
Object Splitting & Merging
It becomes necessary to split touching objects in many images so we’ve provided both automatic watershed and boundary shape-based splitting techniques as well as a manual point-to-point and polyline-based splitting methods to get the job done.
Object naming and coloring
This makes it easier to keep track of what’s what by allowing each object to receive a unique name and color through the editing of the data table. Change a single object or select a large number of objects and change them as a group.
Eliminate objects touching image border
In cases where you only want complete and intact volumes that are not cut off by the image stack’s borders you can enable a clean borders setting to ignore these objects.
The appearance of each object is very important to accurate visualization so parameters for each object are able to be edited including color, transparency, specularity.
Separate objects into custom groups by parameter
Single Variable Classification
Define any number of classification bins, set the first and last values of each bin, and assign classes to objects according to the bin ranges. Use any parameter in the measurement list to classify the objects by that "single" variable.
Define the number of groups to be created, choose all the measurement parameters to be used for the classification, and then apply hierarchical clustering to the objects. Classes will be assigned based on the cluster created by cutting the clustering tree where the number of branches correspond to the number of requested classes.
Classify objects using multiple parameters based on manually selected reference objects. This is an especially useful technique when you are not certain which parameters to use for the classification, but have some idea of how the objects should appear per class.
Generate data for making helpful comparisons
Measure Distances Between Objects
Measure one-to-one and one-to-many distances between objects.
Measure Objects within Objects
Analyze parent/child relationships with tools that allow you to automatically measure and group objects within objects.
Sort Counted Objects
Create a new image displaying all counted objects arranged by size.
Analyze the Spatial Distribution of Objects