Image Data Pop-Up Menu

A pop-up menu is available in the Data Settings and Properties to provide access to tools for cropping, sampling, and converting image data, as well as functions such as image stitching, dataset registration, and image stack alignment.

The following items are available whenever you select a single item in the Data Properties and Settings panel.

Opens the Image Properties panel, in which the basic and advanced properties related to a dataset can be viewed and modified (see Image Properties).

The following options are available to modify and transform image data.

Opens the Dataset Cropper panel. In this panel you can crop image data to the bounding box defined with the Clip box tool (see Cropping Datasets).

Opens the Dataset Sampler panel, in which you can modify image spacing by upsampling or downsampling (see Sampling Datasets).

Opens the Convert Dataset panel, in which you can restructure data according to different analysis needs (see Converting Image Data).

Copies the intersected values of the selected data into the geometry, or shape, of another object to create a new dataset (see Resampling Geometries).

Opens the Invert Dataset panel. In this panel you can invert the values and the X, Y, and Z axes of your image data, as well as apply an axis transformation (see Inverting Datasets).

Applies transformations, such as translations, rotations, and scaling, that were applied to a reference object to the selected data (see Applying Transformations from Other Objects).

The following options are available to align image data with the current scene views.

Automatically rotates image data so that the selected axis is aligned to the Z-axis of the world coordinate system, as shown below.

Automatically aligns the centroid of the selected data with the centroid of another object. Applicable objects include volumetric image data, regions of interest, multi-ROIs, and meshes. Reference objects can be selected in the Choose the Object to Align With dialog.

Lets you to save reformations, such as oblique and double-obliques, that were applied with the Walk tool or by other methods. The result is a new multi-ROI formatted in the current space (see Deriving New Volumes from the Current View).

The following options are available for creating boxes that correspond to the geometry of the selected item or to the item's clip box.

Opens the Create a Box dialog with the geometry set to the bounding box of the selected item.

Note You can edit the geometry before you create the box, as well as modify the Advanced Properties, such as the box's position in space.

Opens the Create a Box dialog with the geometry set to the clip box of the selected item.

Note You can edit the geometry before you create the box, as well as modify the Advanced Properties, such as the box's position in space.

The following options are available for exporting image data.

Lets you export data to a number of different file formats (see Exporting Images).

Exports data in the ORS Object (*.ORSObject extension) file format (see Exporting Objects).

Exports data in the DICOM file format (see Exporting Images in the DICOM Format).

Exports data in the VTK (*.vtk extension) file format.

Exports data in the CZI file format (*.czi extension).

This option is available for Dragonfly Pro only. Contact Object Research Systems for information about the availability of this version of Dragonfly.

Generates a contour mesh from data values above a selected threshold (see Generating Contour Meshes from Images).

Opens the Compare Histograms dialog, in which you can compare the histograms of multiple datasets (see Comparing and Normalizing Histograms).

Lets you apply deep learning regression models that you trained, imported, or downloaded for denoising and super resolution tasks to the selected data (see Filter with AI).

Lets you segment datasets with any of the deep learning or machine learning semantic segmentation models that you trained, imported, or downloaded (see Segment with AI).

Provides a shortcut for selecting macros that can be executed for the selected image data (see Recording and Playing Macros).

Opens the User Data dialog, in which you can view detailed object descriptions, as well as add fields to further describe the selected item (see Managing User Data).

Creates a multi-ROI in which each value in the dataset is assigned as a class. If there are 2,000 values in the dataset, then there will be 2,000 classes in the multi-ROI. Usually done after post-processing, such as creating a distance map, to create a sparse representation of the gray tones in a dataset. You should note that this option is not available for datasets in FLOAT and that values of 0 will not be assigned to as a class.

In some cases, you may want to split non-connected labels that were created from the same data value (see Split Not Connected Labels).

Creates a distance map for a selected region of interest using Dijkstra's algorithm for finding the shortest paths between nodes.

Refer to en.wikipedia.org/wiki/Dijkstra%27s_algorithm for more information about Dijkstra's algorithm.

Creates a distance map for a selected region of interest using the numerical method created by James Sethian for solving boundary value problems of the Eikonal equation.

Refer to en.wikipedia.org/wiki/Fast_marching_method for more information about the fast marching method.

Lets you extract a new dataset from marked image slices (see Extracting New Images from Marked Slices).

Lets you create a 4D, or time-enabled, dataset from multiple datasets (see Creating 4D Images).

Lets you combine, or merge, multiple volumetric datasets into a single 3D dataset (see Stitching 3D Images).

Lets you to register a selected dataset to a baseline by modifying its position and/or rotation (see Image Registration).

Lets you to align the slices within a dataset to build a consistent image stack (see Aligning Image Stacks).

Lets you precisely combine, or stitch, overlapping 2D or 3D image tiles to create a single high-resolution image that is beyond the normal aspect ratio and resolution of a microscope's field of view (see Stitching Images).

Lets you create a new region of interest with the same shape as the selected image data and that is fully labeled within the selected range and increment (see Creating Mask ROIs).

Note Mask ROIs can used for training and applying Deep Learning and Classical Machine Learning models, computing Watersheds, and other computationally expensive tasks.

Automatically creates a new region of interest for each intensity value in the selected dataset.

Automatically computes the path linking two regions of interest (see Finding Paths Between ROIs).

Lets you normalize the histogram of the selected dataset to the histogram of a reference (see Comparing and Normalizing Histograms).

Note This item will only be available if another dataset is loaded.

Lets you make padded copies of image data, regions of interest, and multi-ROIs.

Note A series of dialogs will appear that let you choose the padding for the X, Y, and Z axes, as well as a value for the voxels added to images.

Lets you overwrite the values of an image with the intersecting values of a selected, or reference, image.

Note The reference image and the image to overwrite must be of the same data type — UBYTE, SHORT, UINT, or FLOAT. For best results, the image values should also be within the same range. If required, you can convert or normalize data within a selected range with the Image Converter (see Converting Image Data).

  1. Right-click the image you want to overwrite and then choose Overwrite with Another Image in the pop-up menu.
  2. Choose the dataset you want to overwrite with in the Choose an Image to Overwrite With dialog.

  3. Click OK.

Opens the Bone Analysis Wizard, which is designed for the evaluation of high-resolution micro-CTs of bone specimens (see Bone Analysis).

Opens the Plug Analysis Wizard, which provides a dedicated workflow for analyzing core plugs acquired from rock samples and other porous media.

Opens the Segmentation Wizard, which provides an easy-to-use, guided workflow for implementing powerful deep learning and classical machine learning segmentation of multi-dimensional images (see Segmentation Wizard).

Lets you calibrate the intensity scale of multiple images to a set of calibration standards (see Intensity Scale Calibration).

Lets you automatically update spacing and obtain the correct measurements for images, regions of interest, and multi-ROIs (see Spatial Scale Calibration).

Extracts an object's history as a macro. Extracted macros not only provide an audit trail to help troubleshoot processing issues, but can be edited and replayed to create new objects (see Extracting Object Histories).

Can be used to remove small defects within an image or to replace lost or corrupted parts of image data (see Inpainting).

The following items are available whenever you select two or more items in the Data Properties and Settings panel.

The following options are available to modify and transform image data.

Copies the intersected values of the selected data into the geometry, or shape, of another object (see Resampling Geometries).

The following options are available to align image data with the current scene views.

Automatically rotates image data so that the selected axis is aligned to the Z-axis of the world coordinate system.

The following options are available for exporting image data.

Exports data as multiple files in the ORS Object (*.ORSObject extension) file format (see Exporting Objects).

If the selected items share the same name, their file names will be appended with a sequential number.

Exports all data to a single file in the ORS Object (*.ORSObject extension) file format (see Exporting Objects).

Lets you export selected RBG channels in the BMP, JPEG, and PNG file formats.

Note You can drag channels up and down to match them to a specific output in the Export as RGB dialog, shown below, as well as replace channels as required.

Lets you apply deep learning regression models that you trained, imported, or downloaded for denoising and super resolution tasks to the selected data (see Filter with AI).

Lets you segment datasets with any of the deep learning or machine learning semantic segmentation models that you trained, imported, or downloaded (see Segment with AI).

Provides a shortcut for selecting macros that can be executed for the number of items selected.

Lets you create a 4D, or time-enabled, dataset from multiple images (see Creating 4D Images).

Lets you combine, or merge, multiple volumetric images into a single 3D dataset (see Stitching 3D Images).

Lets you normalize the histogram of the selected dataset to the histogram of a reference (see Comparing and Normalizing Histograms).

Lets you create the following moment datasets from the current selection:

Mean… The first raw moment, which is the mean value of the pixels in the selection.

Variance… The second central moment.

Skewness… The third order moment about the mean. Skewness is a measure of symmetry, or more precisely, the lack of symmetry. A distribution, or dataset, is symmetric if it looks the same to the left and right of the center point.

Kurtosis… The fourth order moment about the mean. Kurtosis is a measure of whether data is heavy-tailed or light-tailed relative to a normal distribution. That is, datasets with high Kurtosis tend to have heavy tails, or outliers. Datasets with low Kurtosis tend to have light tails, or lack of outliers.

Hyperskewness… The fifth central moment.

Hyperflatness… The sixth central moment.

Opens the Segmentation Wizard, which provides an easy-to-use, guided workflow for implementing powerful deep learning and classical machine learning segmentation of multi-dimensional images (see Segmentation Wizard).

Opens the Processed Image Comparator, in which you can evaluate the quality of processed image data with a number of metrics.

Lets you automatically update spacing and obtain the correct measurements for images, regions of interest, and multi-ROIs (see Spatial Scale Calibration).