International Journal of Science, Engineering and Technology Research (IJSETR)
Volume 1, Issue 1, June 2014
Comparison between Edge Detection and K-Means
Clustering Methods for Image Segmentation and Merging
Hnin Mar lar Win, Nang Aye Aye Htwe
Department of Information Technology
Mandalay Technological University
Abstract – Color based image segmentation techniques can
be differentiated into the following basic concepts: pixel
oriented
Contour-oriented,
region-oriented,
modeloriented, colour oriented and hybrid. Segmentation of color
image is a crucial operation in image analysis and in many
computer vision, image interpretation, and pattern
recognition system, with applications in scientific and
industrial field(s).
Color Image Segmentation is the
partition of an image into a set of non-overlapping regions
whose union to the entire image. Merging is combining stage
for segmented regions of an image. This system is to
compare segmentation time and image quality after merging
based on two approaches: Edge Detection and K-Means.
Edge Detection aims at identifying points in a colour image.
These points based on the image brightness changes sharply
or, more formally, has discontinuities. Edge detection is
faster than K-Means in processing time and computation
time. Edge detection used only gradient lines to segment. KMeans clustering used L*a*b color format. This system can
be used in large image transmission, that purpose is faster in
large image transmission speed.
Keywords - Image Segmentation, Sobel Edge Detection,
Gradients, K-Means, L*a*b.
I. INTRODUCTION
For image segmentation, the images that need to be
classified are segmented into many homogeneous areas
with similar spectrum information firstly, and the image
segments’ features are extracted based on the specific
requirements of ground features classification. The colour
homogeneity is based on the standard deviation of the
spectral colours, while the shape homogeneity is based on
the compactness and smoothness of shape.
Digital image processing has many advantages over
analog image processing. Digital image processing allows
a much wider range of algorithms to be applied to the
input data and can avoid problems such as the build-up of
noise and signal distortion during processing. Digital
image processing is commonly used in large image
transmission. In an image: pixels, boundary and edges are
included. These pixels, boundary and edges are convert to
segment region are called image processing.
In imaging science, image processing is any form of
signal processing for which the input is an image, such as
a photograph or video frame; the output of image
processing may be either an image or a set of
characteristics or parameters related to the image. Most
image-processing techniques involved treating the image
as a two-dimensional signal and applying standard signalprocessing techniques to it. Image processing referred to
digital image processing, but optical and analog image
processing also are possible. In this system, two image
processing techniques (edge detection and K-Means
Clustering) are used to system implementation.
The propose system will use two image processing
methods: Edge Detection and K-Means. The edges
identified by edge detection method. To segment an
object from an image, requirement is edge between
closed region boundaries. Edge Detection method find
these edges. Segmented images are not same and similar.
K-Means method is also used for image segmentation. KMeans method reads an image and converts it in L*a*b
format. These segmented images are showed as outputs,
and then merge segmented images into original image.
II. RELATED WORKS
Image segmentation based on K-Means clustering and
edge detection techniques were described by Nassir
Salman, computer science department in Zarqa Private
University. Nassir Salman described to perform image
segmentation and edge detection tasks [1]; there are many
methods that incorporate region- growing and edge
detection techniques. For example, it is applying edge
detection techniques to obtain Difference In Strength
(DIS) map. In combining both special and intensity
information in image segmentation approach based on
multi-resolution edge detection, region selection and
intensity threshold methods to detect white matter
structure in brain. [4] But Nassir Salman desired that edge
detection and K-means are good to use in image
segmentation and merging.
DR. S.K. KATIYAR presents a novel image
segmentation based on color features with K-means
clustering unsupervised algorithm. In this we did not used
any training data. The entire work is divided into two
stages. First enhancement of color separation of satellite
image using de-correlation stretching is carried out and
then the regions are grouped into a set of five classes
using K-means clustering algorithm. Using this two-step
process, it is possible to reduce the computational cost
avoiding feature calculation for every pixel in the image.
Although the color is not frequently used for image
segmentation, it gives a high discriminative power of
regions present in the image.
Edge strength technique to obtain accurate edge maps
of images was presented by Gullanar M.Hadi and Nassir
H.Salman. [3] In this technique: problem of undesirable
over segmentation results produced by the other
algorithms (not contain edge detection and K-Means),
when used directly with raw data images. Also, the edge
maps obtained have no broken lines on entire image.[6]
This paper describes the comparison between edge
detection and K-Means. These two methods are also used
in image segmentation and merging. The system also
compared in processing time, image quality, decrease
All Rights Reserved © 2014 IJSETR
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International Journal of Science, Engineering and Technology Research (IJSETR)
Volume 1, Issue 1, June 2014
over segmentation and others. The system also presented
how to segment and how to merge in image processing.
III. BACKGROUND THEORY
Image segmentation and merging is made by image
processing. Image segmentation is one of the important
steps in image processing. It is based on input image and
then it worked for segment and merge. This paper
describes about K-Mean algorithm and edge detection
algorithm in details. Both two algorithms are server for
image processing. Image processing means for the
proposed system is image segmentation and merging.
The goal of segmentation is to simplify and change
the representation of an image into something that is
more meaningful and easier to analyze.
A. Edge Detection Algorithm
Edge detection referred to the process of identifying
outlines in an image. Edge detection detected these
outlines of an object and boundaries between objects and
the background in the image. In edge detection, sobel
operator is used to image segmentation. The process of
partitioning a digital image into multiple regions or sets
of pixels using outlines is called image segmentation.
Edge is a boundary between two regions with
relatively distinct gray level properties. This gray level
calculated by gradient operator. This system is used sobel
operator techniques in edge detection.
The gradient of an image f(x,y) at location (x,y) is the
vector:
Figure 2. Block Diagram for Image Segmentation using Edge Detection
with Sobel
Edge detection using sobel procedure illustrates with
the following figure 3.
(a)
(b)
(1)
The gradient vector points in the direction of
maximum rate of change of f at (x,y).
Gradient direction of can be calculated as:
(2)
In this equation,‘a’ means different distance from
edge endpoint. The computation of the partial derivation
in gradient may be approximated in digital images. Sobel
operator is shown in the masks below.
Mx
My
Figure 1. The Sobel Masks
Sobel operator found sobel values (X and Y). Purpose
of this case is to generate edge map. Gradient operator
used in finding sobel value X and Y. Then, generate
gradient value from sum of root sobel values. And then,
sobel operator generated edge map of input image.
Finally, sobel operator makes subtraction in original
image (OI) to edge map and generate segmented images.
The following block diagram showed image
segmentation using edge detection with sobel.
(c)
Figure 3. (a) Input Image.
(b) Sobel Operator Detects Edge.
(c) Output Generated as Segmented Images.
The result of merging stage is also based on
segmentation result; because merging is work by the
reverse the segmentation steps. In image segmentation, an
input image is segmented as many images for
foregrounds and background. For merging, results of
segmentation (foreground images and background image)
must combine as an image. This merged image is become
an image but quality is different from original image.
In my opinion, the result of edge detection
segmentation is simply, but segmentation accuracy is
fewer than K-Mean segmentation. Therefore, the user
should choice K-Mean technique for image processing.
B. K-Means Clustering Algorithm
There are many methods of clustering developed for a
wide variety of purposes. Clustering algorithms used for
unsupervised classification of remote sensing data vary
according to the efficiency with which clustering takes
place-means is the clustering algorithm used to determine
the natural spectral groupings present in a data
All Rights Reserved © 2014 IJSETR
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International Journal of Science, Engineering and Technology Research (IJSETR)
Volume 1, Issue 1, June 2014
set. This accepts from analyst the number of clusters to
be located in the data. The algorithm then arbitrarily
seeds or locates, that number of cluster centers in
multidimensional measurement space. Each pixel in the
image is then assigned to the cluster whose arbitrary
mean vector is closest. The procedure continues until
there is no significant change in the location of class
mean vectors between successive iterations of the
algorithms. As K-means approach is iterative, it is
computationally intensive and hence applied only to
image subareas rather than to full scenes and can be
treated as unsupervised training areas.
Color-Based Segmentation Using K-Means Clustering
describes in the following steps. The basic aim is to
segment colors in an automated fashion using the L*a*b*
color space and K-means clustering. The entire process
can be summarized in following steps
 Step 1: Read the image Read the image from mother
source which is in .JPEG format, which is a fused
image of part of Bhopal city of Madhya Pradesh,
India with DWT fusion algorithm of Cartosat-1 and
LISS-IV of Indian satellite IRS-P6 and IRS-1D.
 Step 2: For color separation of an image apply the
Decor relation stretching
 Step 3: Convert Image from RGB Color Space to
L*a*b* Color Space How many colors do we see in
the image if we ignore variations in brightness?
There are three colors: white, blue, and pink. We can
easily visually distinguish these colors from one
another. The L*a*b* color space (also known as
CIELAB or CIE L*a*b*) enables us to quantify
these visual differences. The L*a*b* color space is
derived from the CIE XYZ tristimulus values. The
L*a*b* space consists of a luminosity layer 'L*',
chromaticity-layer 'a*' indicating where color falls
along the red-green axis, and chromaticity-layer 'b*'
indicating where the color falls along the blue-yellow
axis. All of the color information is in the 'a*' and
'b*' layers. We can measure the difference between
two colors using the Euclidean distance metric.
Convert the image to L*a*b* color space.
 Step 4: Classify the Colors in 'a*b*' Space Using KMeans Clustering Clustering is a way to separate
groups of objects. K-means clustering treats each
object as having a location in space. It finds
partitions such that objects within each cluster are as
close to each other as possible, and as far from
objects in other clusters as possible. K-means
clustering requires that you specify the number of
clusters to be partitioned and a distance metric to
quantify how close two objects are to each other.
Since the color information exists in the 'a*b*' space,
your objects are pixels with 'a*' and 'b*' values. Use
K-means to cluster the objects into three clusters
using the Euclidean distance metric.
 Step 5: Label Every Pixel in the Image Using the
Results from K-MEANS For every object in our
input, K-means returns an index corresponding to a
cluster. Label every pixel in the image with its
cluster index.

Step 6: Create Images that Segment the Image by
Color. Using pixel labels, we have to separate objects
in image by color, which will result in five images.
 Step 7: Segment the Nuclei into a Separate Image
Then programmatically determine the index of the
cluster containing the blue objects because K- means
will not return the same cluster_idx value every time.
We can do this using the cluster center value, which
contains the mean 'a*' and 'b*' value for each cluster.
The results of K-Means clustering methods describes by
using the following figures 4.
Figure 4. Steps by Steps of K-Means Clustering Method
C. Differences of Edge Detection and K-Means
Clustering Methods
Table I: Differences of Edge Detection and K-Means
Edge Detection
K-Means
Input Image
JPEG and
JPEG, BITMAP,
Type
BITMAP
GIF, PSD (RGB
Format)
Resolution
should be
should be 150px
300 px
and 400 px
Method for
Sobel
gradient line
Contour
Normal
Closed
To decrease
used gradient
used L*a*b
over
lines
format
segmentation
Method for
only gradient
using clustering
detect image
lines
% of oversegmentation
Processing
Time
All Rights Reserved © 2014 IJSETR
2.6%
Fast
1.5%
Fast
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International Journal of Science, Engineering and Technology Research (IJSETR)
Volume 1, Issue 1, June 2014
In my opinion upon in these differences, both
detection and K-Means Clustering methods
advantages and differences. But, processing
computation time of edge detection is faster
processing time of K-Means Clustering Method.
edge
have
and
than
The segmentation and merging window is shown by
the following figure 7. In this window, the user can load
an image that want to segment and merge.
IV. DESIGN AND IMPLEMENTATION OF THE SYSTEM
The following section describes about the design and
the implementation of the proposed system. The
implemented system used edge detection and K-Means
clustering methods for image segmentation.
A. Design of the Proposed System
The propose system is used to compare between edge
detection and K-Means clustering. methods for image
segmentation and merging. If the system is start, the input
image is income. This image will be segment by two
methods: edge detection and K-Means clustering. The
system shows the segmented result and then merges the
segmented images. The system also compares the results
of two methods: edge detection and K-Means clustering.
Figure 7. Segmentation and Merging window
The input image from user is shown the following
figure 8.
Start
Input Image
Segment image using Sobel
Segment image using K-Mean
Generate segmented images
Generate segmented images
Merge segmented images
Merge segmented images
Compare two results
YES
Figure 8. Input an Image
More
NO
END
Figure 5. Block Diagram
The design of comparison between edge detection and
K-Means clustering for image segmentation and merging
is shown in the figure 5.
After user give an image in the algorithm, the user can
make segmentation and merging by using the input image.
The system is also calculated describes segmentation time,
merging time and image quality.
The segmentation time and quality of edge detection
about sobel edge detection algorithm is shown by the
following figure 9.
B. Implementation
The main window of implemented system is shown by
the following figure 6. This main window include main
menu button and exit button.
Figure 9. Segmentation using Sobel Edge Detection
Segmentation using K-Means clustering window is
shown by the following figure 10.
Figure 6. Main Window of Implemented System
All Rights Reserved © 2014 IJSETR
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International Journal of Science, Engineering and Technology Research (IJSETR)
Volume 1, Issue 1, June 2014
Figure 13: Total Time and Total Quality
Figure 10. Segmentation using K-Means Clustering
The system will show the segmetation results for two
methods: Edge detection and K-Means clustering. In
figure 10: the system also shows and compare the
segmetation time, segmentation quality. Therefore the
user can decide which method is more better than another
method.
The merging using edge detection form is shown in
the following figure 11: include time and quality.
The comparison of edge detection and K-Means
clustering is shown by the table II. Both two algorithms
have advantages and disadvantages in time and quality of
segmentation and merging. Processing time of edge
detection is faster than the processing time of K-Means
clustering. But the segmentation quality and merging
quality of K-Means clustering is better than edge
detection. Therefore, user can be used by choosing in
these two algorithms.
Table II : Comparison of Edge detection and K-Means
clustering.
Figure 11 : Merging using Edge Detection
The merging using K-Means clustering. is shown by
the following figure 12.
Figure 12: Merging using K-Means clustering.
The total time and total quality of the system is shown
by the following figure 13.
V. CONCLUSION
The proposed system is used edge detection technique
and K-Means clustering. technique for image
segmentation and merging. In K-Means clustering, accept
RGB format and convert to L*a*b format and working by
the clustering procedures to detect foreground and
background. The result of K-Means clustering is better
and accurate than edge detection. The processing time of
edge detection method is faster than K-Means clustering.
method. But, the merged image quality of K-Means
clustering is better than merged image quality of edge
detection.
ACKNOWLEDGMENT
I am highly grateful to Dr. Myint Thein, Pro-Rector of
Mandalay Technological University for his permission to
write this paper. I would like to express deepest gratitude
and special thanks to, Dr. Nang Aye Aye Htwe,
Department of Information Technology, Mandalay
Technological University, for her supervising,
enthusiastic and suggestion. I would like to thank to all
All Rights Reserved © 2014 IJSETR
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International Journal of Science, Engineering and Technology Research (IJSETR)
Volume 1, Issue 1, June 2014
teacher from Department of Information Technology,
Mandalay Technological University, who give
suggestions and advices for submission of paper.
[1]
[2]
[3]
[4]
[5]
[6]
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ANIL Z CHITADE and DR. S.K. KATIYAR,
Department of civil Engineering, MANIT, Bhopal,
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Gullanar M.Hadi and Nassir H.Salman, Department
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Journal Innovative Computing, Volume 7, 2011.
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