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# Image Morphological Operations

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## Description

Applies morphological operations to enhance or modify the dataset’s images, updating the Image layer.

The processed image can be added to or subtracted from the existing image, as specified in the operation settings.

Demonstration

- Morphological operations demonstration

Select the morphological operation from the Operation type dropdown. Configure the structuring element size and shape for applicable operations (e.g., dilation, erosion) in the Size or H-value and Strel shape fields.

Choose how to handle the result using radio buttons in the Action to the result selecting:

- None, the resulting image will be direct result of the applied morphological operation
- Add to the image, add the resulting image to the current image
- Subtract from the image, remove the resulting image from the current image

Info

It is possible to enhance the resulting image by multiplying it using a factor provided in the Multiply results edit box.
The resulting image can also be smoothed with Smoothing HSize and Sigma.

Click the Preview button to preview the result,
or use the Continue button to apply the selected morphops to all images.

Tip

use Auto preview to interactively follow results.

## List of available morphological operations

### Bottom-hat Filtering

Computes morphological closing (`imclose`) and subtracts it from the original image, highlighting dark features smaller than the structuring element:

This operation is useful for enhancing dark regions or detecting small dark objects against a lighter background, such as pores or shadows in microscopy images.

Reference

imbothat at Mathworks.com

### Clear Border

Suppresses light structures connected to the image border, removing unwanted edge artifacts:

Ideal for cleaning up datasets with bright boundary effects, ensuring focus on internal features.

Reference

imclearborder at Mathworks.com

### Morphological Closing

Performs dilation followed by erosion, smoothing objects and closing small gaps:

This operation helps connect broken structures or fill small holes, maintaining object shapes in noisy datasets.

Reference

imclose at Mathworks.com

### Dilate Image

Dilates the image, expanding bright regions and shrinking dark areas:

Useful for emphasizing or enlarging features, such as cell boundaries or bright spots, in preparation for further analysis.

Reference

imdilate at Mathworks.com

### Erode Image

Erodes the image, shrinking bright regions and expanding dark areas:

Effective for removing small bright noise or thinning structures, enhancing contrast in dense datasets.

Reference

imerode at Mathworks.com

### Fill Regions

Fills holes (dark areas surrounded by lighter pixels), creating uniform regions:

This operation is suited for correcting dark spots within objects, such as voids in cells or artifacts in solid structures.

Reference

imfill at Mathworks.com

### H-maxima Transform

Suppresses maxima with height less than a specified H value, reducing minor peaks:

Useful for simplifying datasets by eliminating small bright spots while preserving significant maxima, aiding in feature detection.

Reference

imhmax at Mathworks.com

### H-minima Transform

Suppresses minima with depth less than a specified H value, reducing minor valleys:

Helps clean up dark noise or small depressions, enhancing the visibility of deeper minima in intensity profiles.

Reference

imhmin at Mathworks.com

### Morphological Opening

Performs erosion followed by dilation, removing small bright objects and smoothing boundaries:

This operation is effective for noise reduction and shape simplification, preserving larger structures.

Reference

imopen at Mathworks.com

### Top-hat Filtering

Computes morphological opening (`imopen`) and subtracts it from the original image, highlighting bright features smaller than the structuring element:

Ideal for detecting small bright objects, such as particles or highlights, against a darker background.

Reference

imtophat at Mathworks.com

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