Morphological Processing

How is Morphological Processing Performed?

-Certain mathematical logic operations are performed on the image using the structuring element to generate the processed image. -The value of each pixel in the output image is based on a comparison of the corresponding pixel in the input image with its neighbors. -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. -Morphologic operations are especially suited to the processing of binary images and greyscale images.

What is Morphological Processing?

-Powerful tool that can be used to extract features and components from an image. -Used to pre-process or post-process images to facilitate analysis. -A small shape (structuring element) is translated across the image during the course of processing. -Certain mathematical logic operations are performed on the image using the structuring element to generate the processed image.

What is a structuring element? Draw Diagram

2D structuring elements consist of a matrix of 0's and 1's, typically much smaller than the image being processed. The center pixel of the structuring element, called the origin, identifies the pixel being processed. The pixels in the structuring element containing 1's define the neighborhood of the structuring element. Structuring element used to probe the input image. slide 34 for diagram

Describe Morphological Closing

A dilation, followed by an erosion. Removes small holes while preserving shape and size of smaller objects imclose - dilates an image, & then erodes dilated image using same structuring element for both operations

Describe Dilation

Adds pixels to object boundaries in image Dilation Rule Value of output pixel is max value of all pixels in input pixel's neighborhood. E.g. binary image; if any pixels are set to 1, output pixel is set to 1. Dilation can repair breaks and intrusions Watch out: Dilation enlarges objects

Describe Morphological Opening

An erosion, followed by a dilation. Removes small objects while preserving shape and size of larger objects NB imopen - erodes an image, & then dilates eroded image using same structuring element for both operations

Explain the purpose of the bwmorph functions in Matlab and describe the required input parameters for these functions.

BW2 = bwmorph(BW,operation) applies a specific morphological operation to the binary image BW. eg: To get image skeleton: BW3 = bwmorph(BW,'skel',Inf); figure imshow(BW3)

Describe two morphological operations

Dilation adds pixels to boundaries of objects in an image, while erosion removes pixels on object boundaries.

Describe Erosion

Erosion removes pixels from object boundary in image Erosion Rule The value of the output pixel is min value of all pixels in the input pixel's neighborhood. E.g. binary image, if any pixels are set to 0, output pixel is set to 0. Erosion can split apart joined objects Erosion can strip away extrusions Disadvantage: Erosion shrinks objects

Describe matlab function imdilate

IM2 = imdilate(IM,SE) dilates the grayscale, binary, or packed binary image IM, returning the dilated image, IM2. SE is a structuring element object, or array of structuring element objects, returned by the STREL or OFFSETSTREL functions. EXAMPLE: se = strel('disk', 5) creates disk-shaped structuring element with rids 5 pixels

Describe matlab function imerode

IM2 = imerode(IM,SE) erodes the binary image IM, returning the eroded image, IM2. SE is a structuring element object, returned by the STREL function

Describe Morphological Opening by reconstruction

Opening-by-reconstruction is an erosion followed by a morphological reconstruction. Ie = imerode(C,se); Iobr = imreconstruct(Ie,C); figure; imshow(Iobr) title('Opening-by-Reconstruction')

Describe matlab function to create structuring element

SE = strel('cube',W) creates a cube structuring element whose width is W pixels.

Compare regular Opening & Closing with Opening & Closing by reconstruction

The morphological techniques "opening-by-reconstruction" and "closing-by-reconstruction" are implemented to "clean" up the image. Reconstruction-based opening and closing techniques are more effective than standard opening and closing at removing small blemishes without affecting the overall shapes of the objects.