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International Conference on Intelligent Computational Systems (ICICS'2012) Jan. 7-8, 2012 Dubai
Morphology Approach in Image Processing
Satish Pawar and V. K. Banga
Mathematical Morphology, which contains the used operators.
The methodology is split into the following four steps:
• Erosion
• Dilation
• Opening
• Closing
Abstract—Morphological operators transform the original image
into another image through the interaction with the other image of
certain shape and size which is known as the structure element.
Morphology provides a systematic approach to analyze the geometric
characteristics of signals or images, and has been applied widely too
many applications such as edge detection, objection segmentation,
noise suppression and so on. Morphology aims to extend the binary
morphological operators to grey-level images. In order to define the
basic morphological operations such as fuzzy erosion, dilation,
opening and closing, a general method based upon fuzzy implication
and inclusion grade operators is introduced. The fuzzy morphological
operations extend the ordinary morphological operations by using
fuzzy sets where for fuzzy sets, the union operation is replaced by a
maximum operation, and the intersection operation is replaced by a
minimum operation.
A. EROSION
Erosion is one of the basic operators in the area
of mathematical morphology, the other being dilation. It is
typically applied to binary images, but there are versions that
work on grayscale images. The basic effect of the operator on
a binary image is to erode away the boundaries of regions of
foreground pixels .Thus areas of foreground pixels shrink in
size, and holes within those areas become larger.
Keywords—Binary Morphological, Fuzzy sets, Grayscale
morphology, Image processing, Mathematical morphology.
I. INTRODUCTION
T
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Mathematical Morphology is a method for quantitative
analysis of spatial structures that aims at analyzing shapes and
forms of an object. Mathematical morphology is based on set
theory. The shapes of objects in a binary image are
represented by object membership sets. Objects are connected
areas of pixels with value 1, the background pixels have value
0. Binary mathematical morphology is based on two basic
operations, defined in terms of a structuring element, a small
window that scans the image and alters the pixels in function
of its window content: a dilation of set A with structuring
element B enlarges the objects, an erosion shrinks objects.
Fig. 1(a) Simple image
Fig. 1 (b) Image after erosion process
In this process we increase the black pixel in the image
making, it look thinner. Every object pixel that is touching an
background pixel is changed into background pixel.
II. METHODOLOGY
For the extraction of the features of interest were applied
routines of mathematical morphology on images. The
software MATLAB was used as platform for the toolbox of
B. DILATION
Dilation is one of the operators in the area of mathematical
morphology, the other being erosion. It is typically applied
to binary images, but there are versions that work on grayscale
images. The basic effect of the operator on a binary image is
to gradually enlarge the boundaries of regions of
foreground pixels . Thus areas of foreground pixels grow in
size while holes within those regions become smaller.
Satish Pawar is pursuing M. Tech (Electronics and Communication
Engineering) in Department of Electronics & Communication Engineering
from Amritsar College of Engineering and Technology, Amritsar, Punjab,
India (s.pawar65@yahoo.com).
Dr. Vijay Kumar Banga is working as Professor and Head of the
Department of Electronics & Communication Engineering, Amritsar College
of Engineering and Technology, Amritsar, Punjab, India (
vijaykumar.banga@gmail.com).
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International Conference on Intelligent Computational Systems (ICICS'2012) Jan. 7-8, 2012 Dubai
Fig. 2 (a) Simple image
Fig. 2 (b)Image after Erosion
Fig. 2 (c) Image after dilation process
In dialation we increase the white pixel in the image
making, it look broader. Every background pixel that is
touching an object pixel is changed into an object pixel.
Fig. 2 (d) Image after Dilation
In this process we firstly do Erosion and then Dilation. This
method is used to remove the extra white pixels from the
images.
C. OPENING
In morphology, opening is the dilation of the erosion of
a set A by a structuring element B:
D. CLOSING
In mathematical morphology, the closing of a set (binary
image) A by
a structuring
element B is
the erosion of
the dilation of that set,
and
denote erosion and dilation, respectively.
where
Together with closing, the opening serves in computer
vision and image processing as a basic workhorse of
morphological noise removal. Opening removes small objects
from the foreground (usually taken as the dark pixels) of an
image, placing them in the background, while closing removes
small holes in the foreground, changing small islands of
background into foreground. These techniques can also be
used to find specific shapes in an image. Opening can be used
to find things into which a specific structuring element can fit.
and
denote the dilation and erosion,
where
respectively. In image processing, closing is, together
with opening,
the
basic
workhorse
of
morphological noise removal. Opening removes small objects,
while closing removes small holes.
Fig. 4 (a) Simple image
Fig. 3 (a)Simple image
Fig. 4 (b) Image after dilation
In this process we firstly do Dilation and then Erosion. This
method is used to remove the extra black pixels from the
149
International Conference on Intelligent Computational Systems (ICICS'2012) Jan. 7-8, 2012 Dubai
images.
REFERENCES
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Fig. 4 (c) Image after erosion
E. Application and implementation
The application developed allows the user to perform four
main operations to an image: dilation, erosion, opening and
closing. Listed below are a few of the functionalities of the
program:
• Visual inspection of image processing allows the
user to see how the structure image affects the
original image.
• Variable playback speeds allows the user to control
the speed at which the structure image is processed
through the image so a user can see how it affects
the final image.
• User defined structure image lets the user control
what the 3x3 structure image looks like and allows
users the ability to see how different structure
images affect different images.
• User defined images lets the user define an image
up to 16x16. By clicking on the different cells, a
user can setup up an image to their specifications
before processing.
• Rewind functionality enables a user to revert back
to the original image if multiple passes were made
during image processing (such as during opening
and closing).
III. CONCLUSION
We show some digital images to illustrate the effect of
dilation-erosion operators in images. It is known that binary
mathematical morphology dilation expands the image and
erosion shrinks it. Erosion yields a smaller image than the
original and dilation in opposite. This idea can be extended to
grey level imagery. A point to notice here, is the fact that
erosion and dilation, basic operations in binary imagery, can
be extended to grey level. If the image and structuring element
are binary (0 and 1), binary operators, erosion-dilation hold
the same effect that the fuzzy erosion-dilation operation
results in overlapping effects of expands/shrink images. Fuzzy
logic and fuzzy set theory provide many solutions to the
mathematical morphology algorithms. They have extended
way of processing the gray scale images by the help of the
fuzzy morphological operators. Fuzzy set and fuzzy logic
theory is a new research area for defining new algorithms and
solutions in mathematical morphology environment.
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