Basic Morphological Operation Using MATLAB | Digital Image Processing using MATLAB openCV, Python, Computer Vision, Source Cod, With Examples, Matlab programs, Full Codes

Basic Morphological Operation Using MATLAB

Digital Image Processing using MATLAB

Introduction

Image Processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. Image Morphology is an important tool in image processing. It is the study of shapes of object present in the image and extraction of image features. Image features are necessary for object recognition. The fundamental morphological operations include Erosion and Dilation. Opening and Closing are also morphological operators. These operators are considering as basic operations in image processing algorithms.

DILATION

Dilation operator can be applied to binary and grey scale images. The objective of this operator is to enlarge the foreground and shrinks background. It gradually increases the boundaries of the region, while the small holes present in the image become smaller. It increases the brightness of the object.

Fig.1.Example: Dilation

Fig.1.1. Applied Structuring Element

EROSION

In mathematical morphology, erosion is important operation. The aim of erosion operators is to shrinks the foreground and enlarges background. Erosion is used to make an object smaller by removing is outer layer of pixels. After applying the erosion operator on the image, the image becomes darker. This operator takes the image and structuring element as inputs and thins the object.

Fig.2.Example: Erosion

 Fig.2.1. Applied Structuring Element

OPENING

Opening operation is combination of dilation and erosion operations. If A and are two sets of pixels, then in the opening, first erode A by B then dilate the result by B. Opening is the unification of all B objects entirely contained in A.

1.      Similar to Erosion

2.      Less destructive

3.      Spot and noise removal

4.      Erosion next dilation

5.      The same structuring element for both operations.

Input:

1.      Binary Image

2.      Structuring Element.

  

Fig.3.Example: Opening

Ø  Take the structuring element (SE) and slide it around inside each foreground region.

Ø  All foreground pixels which cannot be reached by the structuring element without lapping over the edge of the foreground object will be eroded away

Ø  All pixels which can be covered by the SE with the SE being entirely within the foreground region will be preserved.

Ø  Opening is idempotent, Repeated application has no further effects

CLOSING

Closing operation is a dilation operation followed by an erosion operation. Closing is the group of points, which the intersection of object B around them with object A is not empty.

Fig.4.Example: Closing

Ø  Take the structuring element (SE) and slide it around outside each foreground region.

Ø  All background pixels which can be covered by the SE with the structuring element being entirely within the background region will be preserved.

Ø  All background pixels which cannot be reached by the structuring element without lapping over the edge of the foreground object will be turned into a foreground.

Ø  Opening is idempotent: Repeated application has no further effects

MATLAB program for dilation, erosion, opening, closing and their properties

SOURCE CODE:

clc; clear all; close all; a=imread('C:\Users\natheem\Desktop\images\India_satellite.png'); % Read an input image b=strel('line',11,90); % strel represents morphological structuring element figure(1); imshow(a); title('Original image'); c=imdilate(a,b); figure(2); imshow(c); title('Dilate image'); d=strel('disk',11); e=imerode(a,d); figure(3); imshow(e); title('Erode image') c=imopen(a,b); figure(4); imshow(c); title('Open image'); d=strel('disk',11); e=imclose(a,d); figure(5); imshow(e); title('Close image') %Properties h=[0 1 0;1 1 1;0 1 0]; i1=imdilate(a,h); figure(6); imshow(i1); title('Dilate image pro1'); % pro=properties i2=imerode(a,h); figure(7); imshow(i2); title('Erode image '); i3=a-i2; figure(8); imshow(i3); title('pro3'); i4=i1-a; figure(9); imshow(i4); title('pro4'); i5=i1-i2; figure(10); imshow(i5); title('pro5');

Step by Step Explanation                                      

STEP 1: Type Program on MATLAB window

STEP 2: Save program & Run Program Click ‘Add to path’

STEP 3: You will get Corresponding Output for Morphological Operation

RESULT:

Fig.1.Orignal Image

Fig.2.Dialating Image

Fig.3.Eroded Image

Fig.4.Opening Image

Fig.5.Closing Image

Fig.6.Dialate Image property 1

Fig.7.Erode Image Property 2

Fig.8. Property 3

Fig.9. Property 1

Fig.10.Dialate Image property 1

FUTURE SCOPE

The Morphological Image Processing can be further applied to a wide spectrum of problems including

Ø  Medical image analysis:

Tumour detection,

Regurgitation,

Measurement of size and shape of internal organs.

Ø  Robotics:

Recognition and interpretation of objects in a scene, motion control and execution through visual feedback

Ø  Radar imaging:

Target detection and identification.

Selection of Structuring element for object classification through morphology is still challenging to this technique and has been chosen to be the major direction of the future work.

APPLICATIONS

Morphology is used as a method for image transformation. It has been used for extraction of edges and detection of the characteristic objects in mobile photogrammetric systems to making maps from images taken from a car, called mobile mapping systems Morphology is used mainly for decrease an area of interest and extracting specific objects like road signs. Functions of morphology are also used in detecting sewer pipes defects. Architectural monuments as well as industrial objects have edges and parts which can be possibly detected by usage of mathematical morphology functions.

CONCLUSION

The processing of image is quicker and more cost effective. Morphological image processing described an image processing technique which compact with the shape of features in an image. In this paper application of morphological operators are defined with morphological algorithms. This paper highlighted the Morphological operations such as Dilation, Erosion, and Opening, Closing and morphological procedures such as Boundary Extraction,

Thickening, thinning, Noise removal and pruning which are very useful process or implement any image. Most Application areas of image processing are Medical imaging, Factory Automation, Biometrics, Cinematography, Armed Application. Image Processing applications are present in all domain.