Morphological Operations

Dilate, erode, reconstruct, and perform other morphological operations

Morphology is a broad set of image processing operations that process images based on shapes. In a morphological operation, each pixel in the image is adjusted based on the value of other pixels in its neighborhood. By choosing the size and shape of the neighborhood, you can construct a morphological operation that is sensitive to specific shapes in the input image.

Functions

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Perform Morphological Operations

`imerode`	Erode image
`imdilate`	Dilate image
`imopen`	Morphologically open image
`imclose`	Morphologically close image
`imtophat`	Top-hat filtering
`imbothat`	Bottom-hat filtering
`imclearborder`	Suppress light structures connected to image border
`imfill`	Fill image regions and holes
`bwhitmiss`	Binary hit-miss operation
`bwmorph`	Morphological operations on binary images
`bwmorph3`	Morphological operations on binary volume
`bwperim`	Find perimeter of objects in binary image
`bwskel`	Reduce all objects to lines in 2-D binary image or 3-D binary volume
`bwulterode`	Ultimate erosion

Perform Morphological Reconstruction

`imreconstruct`	Morphological reconstruction
`imregionalmax`	Regional maxima
`imregionalmin`	Regional minima
`imextendedmax`	Extended-maxima transform
`imextendedmin`	Extended-minima transform
`imhmax`	H-maxima transform
`imhmin`	H-minima transform
`imimposemin`	Impose minima

Create Structuring Elements and Connectivity Arrays

`strel`	Morphological structuring element
`offsetstrel`	Morphological offset structuring element
`conndef`	Create connectivity array
`iptcheckconn`	Check validity of connectivity argument

Create and Use Lookup Tables

`applylut`	Neighborhood operations on binary images using lookup tables
`bwlookup`	Nonlinear filtering using lookup tables
`makelut`	Create lookup table for use with `bwlookup`

Pack Binary Images

`bwpack`	Pack binary image
`bwunpack`	Unpack binary image

Topics

Elements of Morphology

Types of Morphological Operations
The most basic morphological operations are dilation and erosion. You can combine dilation and erosion for more specialized operations.
Morphological Reconstruction
Morphological reconstruction is used to extract marked objects from an image without changing the object size or shape.
Structuring Elements
A structuring element defines the neighborhood used to process each pixel. A structuring element influences the size and shape of objects to process in the image.
Border Padding for Morphology
Morphological dilation and erosion pad the image border in different ways to avoid border effects.
Pixel Connectivity
Connectivity determines whether a center pixel and adjacent pixels belong to the same object.
Lookup Table Operations
A lookup table is a vector in which each element represents the different permutations of pixels in a neighborhood. Lookup tables are useful for custom erosion and dilation operations.

Applications of Morphology

Dilate an Image to Enlarge a Shape
Dilation adds pixels to boundary of an object. Dilation makes objects more visible and fills in small holes in the object.
Remove Thin Lines Using Erosion
Erosion removes pixels from the boundary of an object. Erosion removes islands and small objects so that only substantive objects remain.
Use Morphological Opening to Extract Large Image Features
You can use morphological opening to remove small objects from an image while preserving the shape and size of larger objects in the image.
Flood-Fill Operations
A flood fill operation assigns a uniform pixel value to connected pixels, stopping at object boundaries.
Find Image Peaks and Valleys
You can use neighborhood processing to find global and regional minima and maxima in images.

Related Information

Binary Morphology in Image Processing (MathWorks Teaching Resources)

Featured Examples

Detect Cell Using Edge Detection and Morphology

Detect an object against the background using edge detection and basic morphology.

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Marker-Controlled Watershed Segmentation

Separate touching objects in an image by using watershed segmentation.

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Granulometry of Snowflakes

Calculate the size distribution of snowflakes in an image by using morphological opening.

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Correct Nonuniform Illumination and Analyze Foreground Objects

Perform image preprocessing such as morphological opening and contrast adjustment. Then, create a binary image and compute statistics of image foreground objects.

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