Comparative analysis and implementation of structured edge active contour

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 2, April 2020, pp. 1842~1848
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i2.pp1842-1848  1842
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
Comparative analysis and implementation
of structured edge active contour
Brijesh N. Shah1
, Jaymin K. Bhalani2
1
Department of Electronics and Communication Engineering, Charusat University, India
2
Department of Electronics and Communication Engineering, BIT Campus, India
Article Info ABSTRACT
Article history:
Received May 9, 2019
Revised Oct 19, 2019
Accepted Oct 31, 2019
This paper proposes modified chanvese model which can be implemented on
image for segmentation. The structure of paper is based on Linear structure
tensor (LST) as input to the variant model. Structure tensor is a matrix
illustration of partial derivative information. In the proposed model,
the original image is considered as information channel for computing
structure tensor. Difference of Gaussian (DOG) is featuring improvement in
which we can get less blurred image than original image. In this paper LST is
modified by adding intensity information to enhance orientation information.
Finally Active Contour Model (ACM) is used to segment the images.
The proposed algorithm is tested on various images and also on some images
which have intensity inhomogeneity and results are shown. Also, the results
with other algorithms like chanvese, Bhattacharya, Gabor based chanvese
and Novel structure tensor based model are compared. It is verified that
accuracy of proposed model is the best. The biggest advantage of proposed
model is clear edge enhancement.
Keywords:
Active Contour
Difference of Gaussian (DOG)
Image segmentation
Linear Structure Tensor
Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Brijesh N Shah,
Department of Electronics and Communication Engineering,
Charusat University,
Changa, Gujrat, India.
Email: brijeshshah.ec@charusat.ac.in
1. INTRODUCTION
Image segmentation is very essential part of image processing. Basically in segmentation, we
partition original image into several regions, which are useful from our application point of view. Since many
decades several techniques of segmentation were projected. The Active contour Model (ACM) [1, 2] which
was shown by Kass [3], has been demonstrated as good method for image segmentation. The general method
for ACM is to start with initial contour then we can make some rule to find steady state condition. it means
we can reach correct object boundary. In various publish papers ACM differ in how to select initial contour,
various evolution mechanisms and at which time contour should be stopped from other evolution.
The existing ACM [4, 5] can be divided into two main categories i.e. region based and edge based. Edge
based methods used to capture object boundaries in curve evolution. The main problem with these methods is
they work poorly in presence of noise. They cannot segment object when there is hazy boundary.
Another problem with these type of method is that in the beginning the contour must be fixed
nearby the object, else it is very problematic to get proper segmentation result. Region based model mark
complete usage of image geometric analysis as compared to edge based model. There are many benefits of
region based segmentation over edge based segmentation like they are less responsive to noise. Also, they are
not more profound to contour initialization process. In addition to these, they can easily segment images with
weak boundaries. The famous region based method was proposed by Chan-Vese [6] which is based on
Mumford shah function. The main assumption in C-V model [7, 8] is that image intensities are homogeneous

Int J Elec & Comp Eng ISSN: 2088-8708 
Comparative analysis and implementation of structured edge active contour (Brijesh N Shah)
1843
in each region. So, it would not be useful on images which have intensity inhomogeneity [9, 10]. For dense
demonstration of texture Linear Structure Tensor can be used. Structure tensor can be calculated from spatial
derivatives of the image.
LST Structure tensor [11] are matrix representation of partial derivative information It has more
powerful descriptions of local patterns as opposed to the directional derivative through its coherence
measure.it is useful in many applications like corner detection. For a gray scale image the matrix field of
structure tensor is given by
2
0 2
x x yT
x y y
h h h
J h h
h h h
 
    
 
  (1)
where , ,
and Matrix transpose
T
x y
h h h h
h h h
x y x y
T
    
    
    

For avoiding cancellation of opposite signed gradient, when direct integration is performed,
gradient is considered as form of its outer product. In order to make matrix field more immune to noise,
smoothing operation is performed by convolving matrix component with a Gaussian kernel K σ with
standard deviation σ.
 * T
J K h h   
(2)
Where, * indicate convolution operator. An LST model can be used for segmentation of texture images.
The texture [12, 13] type of images which have intensity inhomogeneity cannot be segmented by this model.
Therefore this paper suggest the solution of this problem with a combination of filter based tensor values
to LST.
The chan-vese model
The Chan-Vese model for active contour is a method through which we are able to segment
different types of images. The images which are not segmented by thresholding can also be segmented by
this model. This model is based on Mumford Shah functional for segmentation which is widely used in image
segmentation. This algorithm is also useful to segment the object which does not have clearly defined
boundaries. This algorithm is based on level sets [14-16].
The chan-vese model can give solution by minimizing following energy function
  2
1 2 1 0 1
( )
2
2 0 2
( )
, , . ( ) ( , )
+ ( , )
CV
inside C
outside C
E c c C Length C u x y c
u x y c dxdy
 

   
 


(3)
Here, 1 2, and   are constant, generally 1 2 1   . 1c and 2c are the intensity means of 0 inside C
and outsideC . In order to solve energy minimization problem, level set [17-19] methods is used, in which
level set function ϕ(x,y) is used in place of unknown curve. The problem of minimization can be solved by
taking Euler-Lagrange equation and also by updating level set function ϕ(x,y) by gradient descent method
2 2
1 0 1 2 0 2( ) ( ) ( )div u c u c
t

 
    

   
           
(4)
Here, 1c and 2c updates at each iteration by

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 2, April 2020 : 1842 - 1848
1844
0
1
0
2
( , ) ( ( , ))
( ) ,
( ( , ))
( , )(1 ( ( , )))
( )
(1 ( ( , )))
u x y H x y dxdy
c
H x y dxdy
u x y H x y dxdy
c
H x y dxdy






















(5)
We have observed that chanvese model and Bhattacharya model are not useful for texture images,where as
Gabor based chanvese model works well for texture images but it is not useful for images which have
intensity inhomogeneity. Novel structure method only uses LST for edge enhancement.So that these
algorithm are not giving better accuracy of texture as well as images which have intensity inhomogeneity.
We need to develop some novel technique which give better accuracy of various type of images. Therefore,
the objective of the paper to segment the images having following challenges.
 To segment objects from images with constraints of availability of texture region and intensity
inhomogeneity simultaneously.
 To improve overall accuracy of active contour based segmentation algorithm by incorporating DOG and
LST information inactive contour formulation.
In proposed work ,we have developd hybrid model to improve accuracy of various type of images which is
explained in next section.
2. PROPOSED ALGORITHM
In the proposed method we are using Difference of Gaussians (DOG). The DOG can be explained as
below. DOG is basically featuring enhancement in which we can get less obscured (blurred) image from
the real image. Blurring using Gaussian kernel repress only high frequency spatial data. DOG is a BPF (band
pass filter) that repress all spatial frequency except handful of spatial frequencies that are falls in real gray
scale image. The principle advantage DOG offers is that it can provide better edge visibility. One more
advantage that DOG provides is the removal of noise compare to other algorithms. Also, it is very fast as far
as computational complexity is concerned.
The overall algorithm as follows: Determine DOG Kernal: To determine the kernel, the variance
plays the important role. If ratio between two of kernel is large, image blurring would be more. Therefore in
proposed model, the size of image is used to calculate the kernel variance.
1 1( , ) ( , )G I x y G x y   (6)
( , ) ( , )k kG I x y G x y  
(7)
Calculate Edges using DOG: DOG Kernels are used to calculate the edges of the image by convolving image
with DOG kernels as shown in (8):
1 k 
(8)
Calculate local structure tensor: Medical images can be considered as region of similar texture [20].
Therefore to present its orientation, Structure tensor plays an important role. The structure tensor can be
obtained by calculating image edges. In the proposed model, the edge obtained using DOG are used in
the calculation of Linear Structure Tensor.
, ( ) ( ) ( )k kD x G x G x   
(9)
2 2
, ,( ) ( 1)k kD x k G    
(10)
Apply Active Contour model [21-23] over LST image: Later the LST based orientation Information is used
as an external force in ACM model. The equation (10) can be rewritten using LST as shown in (11).

1845
, ,
2 2( ) ( ( ) ) ( ( ) )1 1 2 2k kdiv D x c D x c
t  
 
     
               (11)
3. EXPERIMENT RESULTS AND ANALYSIS
For accuracy representation of proposed model, we can use Dice Similarity Coefficient (DSC),
which can be defined as follow:
DSC=2TP/(FP+2TP+FN)
Where, True Positive=Number of pixels that detected correctly.
False positive=Number of pixels detected as a member of segmented image but not in ground truth
image.
False negative-Number of pixels that are not detected but part of ground truth image. We have
immplemented this proposed algorithm for texture images, noisy images blur images and images which have
intensity inhomogeneity. These images were taken from berkely database [24]. Figures 1 to 6 shows that
proposed algorithm gives better edge visibility compared to novel structure tensor based Chanvese model [8].
We have compared the proposed model with chanvese model, Bhattacharya model, gabor based chanvese
model and Linear structure tensor based chanvese model for various types of images. It is proven that we got
the best accuracy compared to other existing models which is shown in Table 1.
(a) (b) (c)
Figure 1. (a) Original, (b) NSTCV segmented image, (c) proposed model
(a) (b) (c)
(a) (b) (c)

 ISSN: 2088-8708
1846
(a) (b) (c)
(a) (b) (c)
(a) (b) (c)
Table 1 represents the accuracy of proposed Algorithm. We achieve average 99% accuracy of
segmentation. CV Model [25] fails to segment texture as well as images having intensity inhomogeneity.
Bhattacharya model [26] also not able to segment some images due to texture regions as well as noise within
the image. Gabor and LST based ACM are capable to segment images. However, proposed algorithm
outperforms these models and obtained 99% accuracy amongst all images by using DOG based edge
information in LST.
Table 1. Comparison of accuracy for various model [8, 25-27].
Image
Original
figure
Chan-Vese
Model
Bhattacharya
gradient flow
Model
Gabor based
chanvese model
Novel Structure tensor
chanvese model
Figure 1 (b) to Figure 6(b)
Proposed Model
Figure 1(c) to Figure 6(c)
1 99.43 Failed 99.20 98.43 99.98
2 96.18 94.07 95.71 96.18 99.73
3 Failed 96.29 95.46 98.01 99.99
4 Failed 90.18 99.10 99.20 99.97
5 99.48 Failed 99.51 99.46 99.87
6 96.64 94.69 93.85 96.64 99.26
4. CONCLUSION
There are various methods shown for comparision. Chanvese and bhattacharya model are not so
good when texture type of images are given as input. Gabor based chanvese model can solve the problem of
texture images but when intensity inhomogeneity is there ,it cannot give proper result. Novel structure
tensorchanvese model works well forimages with intensityinhomogeneity. A LST is a good descriptor and

1847
also mathematically easy to implement. But specifically for texture image Gaussian filter will create some
problems. In this paper LST has been modified for removing edge dislocationTexture smotthing can also
been done with this method. But the biggest advantage of proposed method is it uses DOG alongwith LST.
We can also see the accuracy of the proposed model is better than almost any other model in almost
all the cases. The advantage of DOG is that it is less prone to noise. This paper proposed structure tensor
using DOG kernel .The orientation information obtained from Linear Structure Tensor is used in active
contour model to obtain segmentation. We have shown that the proposed model works effectively for various
types of images. We also obtained better Edge enhancement using this method.
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BIOGRAPHIES OF AUTHORS
Brijesh N Shah is a PhD Scholar with the Department of Electronics and Communication
Engineering at C S Patel Institute of Technology–Charotar University of Science and
Technology, Changa, Anand, Gujarat, India. He received Bachelor of Engineering degree in
Electronics and Communication Engineering from Saurashtra University and Master of
Engineering degree in Electronics and Communication Engineering from Dharmsinh Desai
University. He is currently working towards his Ph.D degree at Department of Electronics and
Communication Engineering, Charotar University of Science and Technology. His current
research interest lies in Image Processing
Dr. Jaymin K Bhalani is Professor with the Department of Electronics and Communication
Engineering at Babaria Institute of Technology–Varnama, Vadodara, Gujarat, India. His current
research interests are in Image Processing, Wireless Communication Systemsand Signal
Processing. He has published several papers in national/international conferences and
international journals. He received Master of Engineering degree in Electronics and
Communication Engineering with Specialization of Communication Systems Engineering from
Gujarat University, India. He received Ph.D. degree from MSU, Vadodara.

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