Image Processor

Detecting

Creating a Recipe

Describes the process of creating a segmentation recipe to identify features of interest in an image.

Optimizing Steps

Shows the procedures and applications for optimizing, or objectively determining, image processing parameters.

Frequency Filtering

Demonstrates how to use the Frequency Filter in three different applications.

Memory Steps

Shows examples of setting, calling, and using memory steps in a Recipe.

Smart Cluster™

Demonstrates use of the Smart Cluster function for simple and objective multi-class segmentation.

Color Cluster™

Demonstrates use of the Color Cluster function for simple and objective color segmentation.

Color Deconvolution

Demonstrates use of the Color Deconvolution function to highlight features of certain colors.

Smart Find™

Demonstrates two example uses of Smart Find in Auto Segmentation for powerful and objective feature detection.

Polishing

Calibrating The Scale

Shows how to calibrate the scale (i.e. calculate the pixel size) of your image, so that measurements can be made in physical units of microns, instead of pixels.

Setting Layers

Discusses the power and procedures behind setting recipe steps as Layers.

Setting Chapters

Discusses the advantages and procedures behind recipe Chapters.

Adding Notes & Flags

Demonstrates how to add Notes and Flags to Recipe steps.

Measuring

Generating Measurements

Demonstrates how to generate Global and Feature Measurements from segmented features of interest in an image.

Coloring By Measurements

Demonstrates how to color segmented features according to any of their available measurements.

Local Measurements

Demonstrates how to extract and visualize Local Measurements such as area fraction and thickness.

Measuring Feature Intensities

Demonstrates how to measure aspects of grayscale intensities within selected features.