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Intensity Projection

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Description

Intensity Projection

Generate a 2D intensity projection from the dataset across a specified dimension, affecting the image and corresponding Selection, Mask, and Model layers.

Demonstration

Select a projection method from the Projection Type dropdown and choose the dimension (e.g., Z, T) to project along using the Dimension dropdown. Use the Destination buffer dropdown to specify destination for the results.

Click the OK button to generate the projection, replacing the dataset specified in Destination buffer. Use the Cancel button to close the dialog without changes.

List of available projection calculations

Maximum Intensity Projection

This method projects the voxel with the highest intensity value along the selected dimension onto a 2D image:

  • No additional parameters are required.

The resulting image highlights the brightest features across the dimension, such as peak intensities in a Z-stack or time series. This is useful for visualizing dominant structures or signals in volumetric datasets, such as fluorescent markers or high-contrast objects. The image and layers are compressed into a single 2D slice, preserving the maximum values.

Minimum Intensity Projection

This method projects the voxel with the lowest intensity value along the selected dimension onto a 2D image:

  • No additional parameters are required.

The output emphasizes the darkest regions, making it ideal for identifying low-intensity features, such as voids or background areas in a stack. This projection is often used in contrast to maximum projection to analyze the range of intensities. The image and layers are reduced to a 2D slice containing the minimum values.

Mean Intensity Projection

This method projects the average intensity value of voxels along the selected dimension onto a 2D image:

  • No additional parameters are required.

The resulting image represents the mean intensity across the dimension, providing a balanced view of the dataset’s content. This is useful for summarizing overall intensity trends, such as in time-lapse data or Z-stacks with varying signal strength. The image and layers are consolidated into a 2D slice with averaged values.

Median Intensity Projection

This method projects the median intensity value of voxels along the selected dimension onto a 2D image:

  • No additional parameters are required.

The median projection reduces the impact of outliers, offering a robust representation of typical intensities. It’s particularly effective for noisy datasets, where extreme values might skew mean projections. The image and layers are transformed into a 2D slice with median values, maintaining data integrity.

Focus Stacking

This method generates an extended depth-of-field image from a focus sequence using a noise-robust selective all-in-focus algorithm:

  • No additional parameters are required.

Focus stacking combines slices from a Z-stack to produce a single 2D image where all regions are in focus, ideal for datasets with varying focal planes, such as microscopy images with limited depth-of-field. The algorithm selectively fuses sharp areas, enhancing clarity for visualization or analysis. The image and layers are merged into a focused 2D slice.

Focus stacking reference

  • Pertuz et al., "Generation of all-in-focus images by noise-robust selective fusion of limited depth-of-field images, IEEE Trans. Image Process, 22(3):1242–1251, 2013).

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