SSRG International Journal of Electronics and Communication Engineering (SSRG-IJECE) – Volume 3 Issue 8 – August 2016

ISSN: 2348 – 8549 www.internationaljournalssrg.org Page 107

Applications of Morphological Operators using Image Morphological Algorithms

Sakshi Arora#1

, Rahul Pandey#2

1B.Tech. Scholar,

2Associate Professor,

#Department of Electronics and Communication Engineering,

Poornima Institute of Engineering and Technology, Jaipur, Rajasthan, India- 302022

Abstract—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. This paper covers overview of morphological algorithms which are Boundary Extraction, Thinning, Thickening, Noise Removal and Pruning Algorithm.

Keywords- Morphological operators, Thinning, Thickening, Noise removal and Pruning

I.INTRODUCTION

In Image Processing, the term „image‟ is used to denote the image data that is sampled, quantized and readily available in a form suitable for further processing by digital computers. Image processing is an area that deals with manipulation of visual information. To improve the quality of pictorial information for better human interpretation and to facilitate the automatic machine interpretation of images are the basic objectives of image processing [1]. The field of mathematical morphology contributes a wide range of operators to image processing, all based around a few simple mathematical concepts from set theory applications and its functions. Morphological Operators take a binary image and a mask known as a structuring element as inputs. Then the set operators such as intersection, union, inclusion, and complement can be applied to the images. The basic morphological operators are dilation and erosion [2]. In the dilation, structuring element dilated by the image. Dilation can grows or thick the original image. In the Erosion, structuring element is eroded from the image [3]. Eroded image is smaller than the structuring element. Erosion can shrinks or thinned the original image. Opening and closing are derived from the erosion and dilation [4][5]. They are usually applied to binary image. In imaging applications,

morphological operators are widely useful as such they have the wide application range. They can demodulate the boundary[6], identify components, remove noise etc. Some useful applications of morphological operators are described in this paper by morphological algorithms. It processes the efficient applications of morphological operators as such the algorithms are followed to work in the best way.

This paper includesfive sections. Next section describes morphological operators, section 3 introduces basic morphological algorithms [7][8] and section 4 and 5 describes the application and future scope of morphology image processing.

II. MORPHOLOGICAL OPERATORS

2.1 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. This can be defined mathematically

as-

A Ɵ B = {w: Bw A} ……………..(1)

Fig.1. Result of erosion operator (a) Original Image (b) Result

with 3×3 mask (c) Result with 5×5 mask [1]

SSRG International Journal of Electronics and Communication Engineering (SSRG-IJECE) – Volume 3 Issue 8 – August 2016

ISSN: 2348 – 8549 www.internationaljournalssrg.org Page 108

2.2 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.

The dilation of A by B can be denoted as-

A B = {(x, y) + (u. v) : (x, y) Ɛ A, (u, v) Ɛ

B}……(2)

Fig.2. Original Image

Fig.3.After Dilation Operator Dilated Image [1]

2.3 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 [5]. This operation can be

defined as-

…………… (3)

Fig.4. Illustration of Opening Operation

(a) (b)

Fig.5. (a) Original Image (b) Image after Opening

Use of this operator is smoothing the edges, breaking

the narrow joints or separates the objects and thinning

the protrusions that are present in an image.

2.4 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. This can be denoted as-

A • B= (A B) Ɵ B ………….… (4)

(a) (b)

Fig.6. (a) Original Image (b) Image after closing

Closing is useful for smoothing sections of contours,

eliminates small holes and fills gaps in contours.

III. BASIC MORPHOLOGICAL

ALGORITHMS

In practical use of morphology some algorithms are

proposed. These algorithms include extracting image

components to represent and describe the shape of

image; extracting boundaries, thinning, thickening,

pruning etc. Some of the basic algorithms described in

following section:-

3.1 BOUNDARY EXTRACTION

Boundary is the difference between the original image

and eroded image. Boundaries are two types- Internal

Boundary and External Boundary. Let assume that A

is original (input) image and B is structuring element.

Internal Boundary defined as-

β (A)= A - (AƟB) ……………….(5)

External Boundary defined as-

β (A)= (A B) – A ……………...(6)

SSRG International Journal of Electronics and Communication Engineering (SSRG-IJECE) – Volume 3 Issue 8 – August 2016

ISSN: 2348 – 8549 www.internationaljournalssrg.org Page 109

Fig.7. Illustration of Boundary Extraction Algorithm [4]

Fig.8. Example of Boundary Extraction

This operation is useful for removal of unwanted &

achieving scale features of the binary object. Size of

the shape gets reduced by using this operation.

3.2 THINNING

It is used to remove inappropriate foreground pixels

present in the images. This operation is applied only to

binary images. The object of this is to tidy up all the

lines to a single pixel thickness [2]. The performance

of the thinning algorithm depends on the nature of the

structuring element. This can be defined as-

Thinning (A, B) = A- Hit or miss transform (A,

B)……. (7)

Here subtraction is logical subtraction that is, a set

difference operation.Thinning is a single pass

algorithm. In reality, this operator is applied

repeatedly till a condition of convergence is achieved.

(a) (b)

Fig.9. (a) Original Image (b) Image after

Thinning

3.3 THICKENING

This is a dual morphological operation of thinning

operation. This operation is also related to the Hit or

miss transform and is used to grow some selected

foreground pixels in binary images. Thickening

operation can be defined as-

Thickening (A, B) = A U Hit or miss transforms(A,

B)….. (8)

Here A is the image and B is structuring element.

3.4 NOISE REMOVAL

Noise is a disturbance which causes fluctuations in the

pixel values. If the image is corrupted by impulse

noise, morphological operations are useful in

removing such noise. Impulse noise is caused by

sudden disturbance in the image signal [2]. The

morphological opening followed by a closing

operation can remove the noise.

(a) (b)

Fig.10. (a) Image with noise (b) Image after Noise Removal

3.5 PRUNING

Morphological operations create some tail pixels that

affect the topology of the object. These pixels are also

called spurs or parasitic components. Pruning is the

process of removing these extra tail pixels. This

process is an extension of the thinning. The standard

pruning algorithm will remove all branches shorter

than a given number of points. The algorithm starts at

the end points and recursively removes a given

number of points from each branch. After this step it

will apply dilatation on the new end points with a

(2N+1) (2N+1) structuring element of 1‟s and will

intersect the result with the original image [7]. If a

parasitic branch is shorter than four points and we run

the algorithm with n = 4 the branch will be removed.

The second step ensures that the main trunks of each

line are not shortened by the procedure.

SSRG International Journal of Electronics and Communication Engineering (SSRG-IJECE) – Volume 3 Issue 8 – August 2016

ISSN: 2348 – 8549 www.internationaljournalssrg.org Page 110

IV. APPLICATIONS

Morphology is used as a method for image

transformation works in close range photogrammetric

for long years. 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 e.g.

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.

V. FUTURE SCOPE

The Morphological Image Processing can be further

applied to a wide spectrum of problems including:-

a) Medical image analysis: Tumour detection,

measurement of size and shape of internal

organs, Regurgitation, etc.

b) Robotics: Recognition and interpretation of

objects in a scene, motion control and

execution through visual feedback

c) Radar imaging: Target detection and

identification.

This is further extended to Color image concept and 24-

bit True Color concept and a special feature such as

Automatic 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.

VI. CONCLUSION

The processing of image is faster and more cost

effective. Morphological image processing described

an image processing techniques which deal with the

shape of features in an image. In this paper application

of morphological operators are described with

morphological algorithms. This paper highlighted the

Morphological operations (dilation, erosion, and

opening, closing) and morphological algorithms

(Boundary Extraction, Noise removal, thickening,

thinning, and pruning) which are very useful process

or implement any image. Most Application areas of

image processing are Biometrics, Medical imaging,

Factory automation, Photography, Military

Application. Image Processing applications are

present in all domain.

ACKNOWLEDGEMENT

The authors are thankful to Mr. Rahul Pandey,

Assistent Professor, Electronics & Communication

Engineering Department, Poornima Institute of

Engineering & Technology, Jaipur and Dr. Ajay

Kumar Bansal, Director, Poornima Institute of

Engineering & Technology, Jaipur.

REFERENCES

[1] S.Sridhar, “Digital Image Processing”, Seventh Edition, Oxford, 2015

[2] J. Gil and R. Kimmel, “Efficient dilation, erosion, opening and closing algorithms in Mathematical Morphology and its Applications to Image and Signal Processing” V, J. Goutsias, L. Vincent, and D.Bloomberg, Eds. Palo-Alto, USA, June 2000, pp. 301.310, Kluwer Academic Publishers.

[3] Sunil Sharma ,Naveen Kumar Jain and SumanSankhla, “Digital Image Processing”, First Edition, Genius Publication, 2015.

[4] A.M.Raid, W.M.Khedr, M.A.El-dosuky and Mona Aoud, “Image Restoration based on Morphological Operations”, vol. 4, no. 3,june 2014.

[5] R.C.Gonzales, R.E.Woods, “Digital Image Processing”, 2-nd Edition, Prentice Hall, 2002.

[6] H.Heijmans, “Morphological image operators”, Advances in Electronics and Electron Physics.

[7] GlebV.Tcheslavski, “Morphological Image Processing: Basic concepts”, 4304/5365 DIP, Spring 2009.

[8] GlebV.Tcheslavski, “Morphological Image Processing: Basic Morphological Algorithm”, 4304/5365 DIP, Spring 2009.

[9] I.Pitas,”Digital Image Processing Algorithm and

Applications”John Wiley and Sons, Inc. 2002. [10] Serra,J.,”Image Analysis and Mathematical Morphology”,

vol.1, Academic Press, London, 1982.