A STUDY AND ANALYSIS OF DIFFERENT EDGE DETECTION TECHNIQUES

Sundarapandian et al. (Eds) : ITCS, SIP, CS & IT 09,
pp. 39–50, 2013. © CS & IT-CSCP 2013 DOI : 10.5121/csit.2013.3105
A STUDY AND ANALYSIS OF
DIFFERENT EDGE DETECTION
TECHNIQUES
Gullanar M. Hadi1
and Nassir H. Salman2
1
Department of Software Engineering , Salahaddin University,Erbil, Iraq
gullanarm@yahoo.com
2
Department of Computer Science , Cihan University, Erbil, Iraq
nahu64@yahoo.com
ABSTRACT
In the first study [1], a combination of K-means, watershed segmentation method, and
Difference In Strength (DIS) map were used to perform image segmentation and edge detection
tasks. We obtained an initial segmentation based on K-means clustering technique. Starting
from this, we used two techniques; the first is watershed technique with new merging
procedures based on mean intensity value to segment the image regions and to detect their
boundaries. The second is edge strength technique to obtain accurate edge maps of our images
without using watershed method. In this technique: We solved the problem of undesirable over
segmentation results produced by the watershed algorithm, when used directly with raw data
images. Also, the edge maps we obtained have no broken lines on entire image. In the 2nd study
level set methods are used for the implementation of curve/interface evolution under various
forces. In the third study the main idea is to detect regions (objects) boundaries, to isolate and
extract individual components from a medical image. This is done using an active contours to
detect regions in a given image, based on techniques of curve evolution, Mumford–Shah
functional for segmentation and level sets. Once we classified our images into different
intensity regions based on Markov Random Field. Then we detect regions whose boundaries are
not necessarily defined by gradient by minimize an energy of Mumford–Shah functional for
segmentation, where in the level set formulation, the problem becomes a mean-curvature which
will stop on the desired boundary. The stopping term does not depend on the gradient of the
image as in the classical active contour. The initial curve of level set can be anywhere in the
image, and interior contours are automatically detected. The final image segmentation is one
closed boundary per actual region in the image.
KEYWORDS
Watershed, difference in strength map, K-means, edge detection, image segmentation. Active
counters, Level set method, Markov Random Field

40 Computer Science & Information Technology (CS & IT)
1. INTRODUCTION
Edges are boundaries between different textures. Edge also can be defined as discontinuities in
image intensity from one pixel to another. The edges for an image are always the important
characteristics that offer an indication for a higher frequency. Detection of edges for an image
may help for image segmentation, data compression, and also help for well matching, such as
image reconstruction and so on. In the method of image segmentation, we focus on the idea that
edges define boundaries and that regions are contained within these edges. Edge detection refers
to the process of identifying and locating sharp discontinuities in an image. There are many
methods to make edge detection as follows:
To perform image segmentation and edge detection tasks, 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 then employ region growing
techniques to work on the map as in [1] and [2]. In [3], combining both special and intensity
information in image segmentation approach based on multi-resolution edge detection, region
selection and intensity threshold methods. As in [4], Pappas considered the problem of
segmenting images with smooth surfaces, which presents a generalization of the K-means
clustering algorithm to include special constraints and to account for local intensity variations in
the image. Qixiang Ye et al. [5] have proposed to find main edges meanwhile filter edges within
texture regions. They have computed pixel similarity degree around a pixel, have computed a new
gradient, and applied a Canny like operator to detect and locate edges. Caragea [6] detects the
difference between pairs of pixel around a pixel and uses the highest value from the difference of
four pairs of pixels that can be used to form a line through the middle pixel. Al-amri et al. [7]
presented methods for edge segmentation of satellite image: they used seven techniques for this
category; Sobel operator technique, Prewitt technique, Kiresh technique, Roberts technique,
Laplacian technique, Canny technique and Edge Maximization Technique (EMT) and they are
compared with one another so as to choose the best technique for edge detection segment image.
P. Thakare [8] discussed about some image segmentation techniques like edge based, region
based and integrated techniques and explains in brief the edge based techniques and their
evaluation. They also focuses on edge based techniques and their evaluation.
2. LEVEL SET METHOD
In mathematics, a level set of a real-valued function f of n variables is a set of the form:
{ (x1,...,xn) | f(x1,...,xn) = c } …………….……..(1)
where c is a constant. That is, it is the set where the function takes on a given constant value.
When the number of variables is two, this is a level curve (contour line), if it is three (in 3-D ;
Surface (Interface) evolution), this is a level surface, and for higher values of n the level set is a
level hyper surface. So level set methods are used for the implementation of curve/interface
evolution under various forces . In this method many modified functions were used: [4] and [9]

Computer Science & Information Technology (CS & IT) 41
a) Function evolve2D () was used which is a high level function that takes an input, evolves it N
iterations and returns the result. b) Function contour (Z) is a contour plot of matrix Z treating the
values in Z as heights above a plane. A contour plot is the level curves of Z for some values V.
The values V are chosen automatically. The contours are normally colored based on the current
color map. c). Function imcontour; where imcontour (I) was used to create contour plot of image
data and to draw a contour plot of the intensity image I. We found that 50 iterations are very
good for some images, and the evolution type depend upon the parameters values in function
Phi[4][9]:see Figure (6)
phi = evolve2D(phi,dx,dy,0.5,25,[],[],0,[],0,[],[],1,b); also the processing time depend upon
iteration numbers.
3. EDGE STRENGTH MERGING PROCESS
Two edge strengths gradient values (T1, T2) were used in one subroutine, T1 is less than T2. For
example if we choose T1 = 1 and if the Edge strength as in equation (2) is less than 1, we get
merging of every two adjacent regions because the watershed algorithm [10] we used based on
immersion procedure and in this procedure it looks to the topographic surface. It means we
related intensity values as an altitude (height) and we got merging results by comparing the
gradient values of the edge points (pixels) between the two regions and the region itself. If the
points have low gradient values, that means the merging was done and the region becomes large.
So; in this procedure it is very important and useful the choosing values of T1, T2 in our merging
process. See the results in Figure 3.
N
pGradient
strengthEdge Edgep
∑∈
=
)(
(2)
Where Gradient (p) represents edge points gradient values which come from the gradient image
step for all pixels (p) on the edge between every two regions, and N are the number of edge
pixels.
3.1 Edge Strength Merging Process results
(a) Original image of Brain image (512x512). (b) Seg. image into 6 regions by k-means method.

(c) Then segmented image with edges (region map) by watershed algorithm.
(d) An accurate edge map after watershed & merge process by mean value.
Figure 1. The results of K-means method, watershed algorithm, and merging techniques.
[cpu = 27.835s. [20.936 s for K-means. 6.229 s for watershed and merging. And 0.670 s for two
edge strength].
4.DIFFERENCE IN STRENGTH TECHNIQUE RESULTS
The DIS for each pixel was calculated using equation (3) [11]. And after processing all the input
pixels, the DIS map was obtained. In DIS map, the larger the DIS value is, the more the pixel is
likely located at the edge. At this step, a 3x3 window runs pixel by pixel on the input image.
When the window runs over the bolder of the input image, pixels outside the bolder are given the
gray level of the input nearest to it. The DIS for the center pixel as in Figure 2, for example, was
calculated as in equation (3) [11].
)3(....................8272625242
8171615131
ZZZZZZZZZZ
ZZZZZZZZZZ
−+−+−+−+−+
−+−+−+−+−
8675658474
5483736343
ZZZZZZZZZZ
ZZZZZZZZZZ
−+−+−+−+−+
−+−+−+−+−

Figure 2. The DIS detecting windows.
Examples of DIS maps are shown in Figure 3-b. One can expect that the values of DIS should be
small in the smooth regions obtained by k-means. The greater DIS value represents that the
pertaining pixel is on the area that changes severely in gray levels. With the DIS map one can
check with the result of image segmentation based on K-means. It is clear that the DIS map
consists of all edge information about the input image even on the smooth regions.
Since the DIS of the smooth region is small (weak edge), one can use a threshold T to eliminate
false edges and thus obtain larger regions. In this case, the DIS map provides the complete edge
(strong and weak) information about the image. By exploiting these information, one can
accurately locate the contour of an object. Now to find the effect of DIS, we used
multithreshoding edge detection; first we calculated DIS for each pixel in the image then we
calculated the mean value of DIS for the whole image. From the mean value we thresholded our
image by different % of mean DIS. The threshold for discarding weak edges is set to the mean of
DIS as in Figure 5 (d through g).
The threshold used for connected edges is set to the 50% of mean DIS. So, using multi-threshold
is important to eliminate false edges and thus obtain larger regions as in Figure 3 (d through g).
The region map without threshold is shown in Figure 3-c. As we can see from the Figure 3 (d-g)
compare with the Figure 3-c, the concept that an object should have a closed contour help us to
eliminate redundant edge pixels and connect the broken contour by using multi-threshold based
on different values of mean DIS of the whole image under study. But if we take k-means and then
DIS with 25% of mean DIS, we will get all the edges of our images as in the Figure 4 below and
we don't need to use watershed technique.
Z1 Z2 Z3
Z4 Z5
Z6 Z7 Z8
Z1 Z2 Z3
Z4 Z5
Z6 Z7 Z8

(b) DIS map of image (a).(a) Original image.
(d) Threshold 25% of DIS
mean.
(c) Without threshold.
(f) T 100% of DIS.(e) T 50% of DIS
mean.
(g) T 120% of DIS mean.
Figure 3. DIS map of image and multithreshoding of DIS mean edge detection results.

4.1 Other DIS Method Results
The experimental results are shown in Figures (1, 3) above and Figure (4) below. Medical images
as brain images are simple pattern images with the size of {156 x 156} and 256 gray levels
images and other images to test our segmentation and edge detection methods. We obtained
output images consist of all edge information and regions about the input image.The region maps
are shown in Figure1-c. As can be seen from the edge maps Figure 1-d, that there are no broken
lines on the whole image regions. The output image was displayed as an edge map as in Figure 1-
d, Figure 3(e through g); and Figure 4-c.
(b) After K-means process.(a) Original image.
(c) Using threshold 25% of mean DIS.
Figure 4. Edge map using K-means process and thresholding 5% of mean DIS.
5. ACTIVE CONTOUR RESULTS BASED ON LEVEL SET
The basic idea in active contour models or snakes is to evolve a curve, subject to constraints from
a given image, in order to detect objects in that image. For instance, starting with a curve around
the object to be detected, the curve moves toward its interior normal and has to stop on the
boundary of the object. In the classical snakes and active contour models (see [12], [13],[14],
[15]), an edge-detector is used, depending on the gradient of the image 0u , to stop the evolving
curve on the boundary of the desired object.during our process, we used initial segmented images
(different intensity regions) based on Markov Random Field to superimpose the region boundary
and to extract the bounded region (segmented map) in our image as in Figure. 5 (b & c) as an
example.

Original image (b) Segmented image by (c) Segmented map of
M-Shah GAC method bounded area in (b) (extracted region )
(d) Abdomen image after (e) Step1 initial curves (f) Step 2
MRF method
(g) Step3 (h) Semi final results (i) Brain image after MRF

(j) Initial curves (k) Final results
Figure . 5 Segmentation results by Mumford-Shah Geodesic Active Contours (GAC)
So we superimposed the edge of the different regions in the image using Mumford-Shah method
after we chose few closed curves represent different intensity area in our image. For example as
shown in Figure.5 (e&j). So our results accuracy depend on, if the results of MRF is accurate
then the regions boundaries are in correct position as shown from the figures above. Also in this
method; if we want to choose any region in the image and to define its edge, we can do all that.
Then we can calculate some region information such as the area of that region , region map and
contour length clearly. We can use different kind of images to extract different features (roads ,
rivers , agricultural areas …etc ) as in remote sensing images.
6. LEVELSET METHODS RESULTS USING MATLAB AS IN FIG(6)
Figure 6. The final results of levelset method to brain image and elapsed time of processing
(28.5 sec)

7. CONCLUSION
In DIS method:, the segmentation regions and their boundaries were defined well and all of the
boundaries are accurately located at the true edge as shown clearly from Figure1-(c, d), Figure 3-
g, and Figure 6-c. And if we take k-means first and then DIS with 25% of mean DIS, we will get
all the edges of our images as shown in the Figure 4 above, so we need’nt to use watershed
technique.
Also we concluded that using multi-threshold is important to eliminate false edges and thus
obtain larger regions, the DIS map consists of all edge information about the input image even on
the smooth regions, and the combination of k-means, watershed segmentation method, DIS map
are good techniques to perform image segmentation and edge detection tasks, where the final
segmentation results are one closed boundary per actual region of the image under study, and the
two edge strengths gradient values (T1, T2), T1 is less than T2, are very sensitive to get good
results. Where the incorrect choosing of these values gives us uncorrected image segmentation
and edge detection results and this is a disadvantage. So we will develop this work in future with
automatically determined the threshold values.
Finally, the disadvantages of these techniques are depending mainly on k-means results, where if
the clustering procedure doesn't implement correctly, the results are incorrect by the other
techniques we used. However, in this paper we solved the problem of undesirable
oversegmentation results produced by the watershed algorithm, also the edge maps we obtained
have no broken lines on entire image.
In levelset method: we conclude that the levelset technique and extraction an object methods give
us very accurate and clear results. We found that 50 iterations are very good for some images, and
the evolution type depend upon the parameters values in function Phi[4,8]:see Figure (6)
phi = evolve2D(phi,dx,dy,0.5,25,[],[],0,[],0,[],[],1,b); also the processing time depend upon
iteration numbers. In the other study using an active contours based on techniques of curve
evolution, Mumford–Shah functional for segmentation and level sets is a good and accurate
method to detect object(region) boundaries, to isolate and extract individual components from
our image. It is possible to detect objects whose boundaries are not necessarily defined by
gradient by minimize an energy of Mumford–Shah functional for segmentation which can be
seen as a particular case of the minimal partition problem where the stopping term does not
depend on the gradient of the image, as in the classical active contour and the initial curve of
level set can be anywhere in the image . This help us to obtain the final image segmentation is
one closed boundary per actual region in the image where the segmentation problem involves
finding the closed curve C that lies along the boundary of the object of interest in the image. Then
it is easy to calculate the region area and the boundary length. The level set approach allows the
evolving front which can extract the boundaries of particularly intricate contours. Also we can
use this method with different kind of images (e.g., medical images and remote sensing images)
to detect object boundaries, to isolate and extract individual components (as a segmented maps)
as shown as an example in Figure 5 (c).finally Edge detection refers to the process of identifying

and locating sharp discontinuities in an image. The discontinuities are abrupt changes in pixel
intensity which characterize boundaries of objects in a scene.
References
[1] Salman N and Liu C. Q.,(2003) “ Image Segmentation and Edge Detection Based on Watershed
Techniques”, International Journal of Computers and Applications, Vol. 25, No. 4, pp. 258-263.
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[3] Chowdhury, Mahbubul Islam; Robinson, John A.,(2000) IEEE Proc. of Canadian Conference on
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Author
Gullanar M. Hadi, born in 1964. She received her B.Sc. and M.Sc. degrees from
Collage of Science, Al-Mustansyriah University, Baghdad –IRAQ in 1985 and
1989 respectively. She received his PhD degree in Opto-Electronics engineering
from Shanghai Jiao Tong University-China in 2004. Her research interests include
image processing and nano-materials. From 1985-2006, she worked at Al-
Mustansyriah University , collage of Science, Baghdad –IRAQ, from 2006-2009
she worked as a lecturer in several Jordanian University, from 2009 to present she
was lecturer in Software Engineering dept. , Salahaddin University, Erbil-Iraq.
Corresponding author: SALMAN, N. H, born in 1960. He received his B.Sc. and
M.Sc. degrees from Al-Mustansyriah University / Collage of Science in 1983 and
1989 respectively. He received his PhD degree in Image Processing and Pattern
Recognition from Shanghai Jiao Tong University-China in 2002. His research
interests include remote sensing, image processing and image analysis based on
image segmentation and edge detection techniques. Also he is interesting in
computer programming languages, Matlab programming. From 1982-1998, he
worked at Space Research Center -Remote Sensing Dept. in Baghdad –IRAQ, also
he was an assistant professor (from 2002) in the Dept. of Computer Science-Zarqa
Private University-Jordan. Now he is a head of Computer Science dept .Cihan University, Erb

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