International Journal of Engineering Trends and Technology (IJETT) – Volume 12 Number 8 - Jun 2014
Image Segmentation Based on the Edge Detection
with Snakes Contouring and Ant Colony
Optimization
Kirandeep kaur-MTech(IT)
Department of Computer Science & Technology, Lovely professional University, Phagwara, Punjab, India
Abstract--Image segmentation means dividing an image into two
parts which are loosely connected. Segmentation is on the basis of
Edge-Based and Region-Based Techniques. Edge detection filters
the important features in an image from the edges and detects the
edges where there are some changes of intensity. It has many
applications and based on lots of image analysis. Medical images in
image processing are one of the most interesting and challenging
topics in the systematic investigation field. Image segmentation is to
filter out many features of an image i.e. used for image analysis.
MRI (Medical Resonance Image) has an important role in medical
diagnostics. The proposed technique is combination of “snakes”
contouring and Ant colony optimization techniques. Snake is a
multistage process and ACO is probabilistic process which takes
the decision through the movement of number of ants. In the
experimental results, it’s provided to demonstrate the quality
performance of the proposed technique.
images inside the body. This technique is used in every hospital
for medical diagnosis. Snakes find the contour that best
approximates the perimeter of an object. Snakes are active
contour models. It represents some salient features of MRI as a
parametric curve. It is like a rubber band. User makes any curve
close to the object boundary and then starts moving towards the
desired object boundary. In ACO, Pheromone is a parameter
which determines the intensity of the trail or probability.
II. SEGMENTATION
The segmentation might be gray-level, texture, colour, depth or
motion etc.In segmentation, gray level images can provide
useful information of the surfaces where some action occurs.
Example of Segmentation as:
Keyword-- Segmentation, Edge detection, Ant colony optimization,
Snakes contouring.
I.INTRODUCTION
Segmentation has various techniques such as Edge-Based,
Region–Based and Adaptive thresholding based segmentation.
Image segmentation divides an image into set of area [3]. It
includes various applications in the field of medical imaging,
satellite imaging, movement detection; surveillance etc.The
main objectives of segmentation are (i) Break down the image
into regions. (iii)Change of representation of an image. Edges
occur when we have a boundary between two regions in an
image. Edge detection is also known as “Extraction of edges”.
This detects boundary between background and objects in the
image where the brightness of image changes abruptly. This
includes various methods such as Roberts, Prewitt, Sobel,
Canny, Laplacian of Gaussian (LOG) etc [4]. MRI investigates
anatomy and function of the body in health and disease. And its
scanners use strong magnetic fields and radio waves to make
ISSN: 2231-5381
Fig.1Example of segmentation
Complexity of the images is a challenging problem in medical
imagery in segmentation i.e. Brain tissue [7].For Brain’s
segmentation, we can use the two techniques such as image
processing and model-based techniques [7]. Our hybrid method
follows the first image processing steps via a threshold step
which removes small connections between the brain and
surrounding tissue. Then model-based used to take out the eyes
and other features of other nonbrain and recover some of the
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International Journal of Engineering Trends and Technology (IJETT) – Volume 12 Number 8 - Jun 2014
terminated tissue by morphological dilation, a last refinement of
the brain contour (i.e.”Snakes” active contour algorithm).
-1
-1
-1
III.EDGE DETECTION
Edge detection is used to reduce data of an image and produce
image outlines. Edges are those pixels where intensity of image
abruptly or suddenly changes. It simply characterizes boundaries
of objects in image. Geometric events (surface discontinuities,
depth
discontinuities,
color
discontinuities,
texture
discontinuities) and non-geometric events (illumination changes,
secularities, shadows, inter reflections) are the main reasons for
the intensity change. To determine the edge strength and
direction of maximum intensity change at one point and this is
perpendicular to the direction of the gradient vector at that point.
In the following figure, we are describing edge strength and
edge normal. Edge normal is unit vector in which intensity
change is maximum and the vector which is perpendicular to
this edge normal vector is known as edge direction [9].
1
0
0
1
1
1
1
0
-1
1
0
-1
1
0
-1
Robert edge detector: Robert method detects the edges using
derivative. When the gradient of the image is maximum then it
returns edges at those pixels.
1
0
0
-1
0
-1
1
0
Prewitt edge detector: Prewitt depends upon the idea of central
difference only in one direction and less susceptible to noise [4].
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1
0
0
1
1
1
1
0
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0
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IV.PROPOSED METHOD
(A)Snakes contour: In this algorithm, we use three-stage
method. Firstly, we eliminate the background noise
(f)=
Fig.2Edge normal and Edge direction
The following methods are using IST derivative and 2nd
derivative which is defined as gradient operator and gradient
direction [10] as:
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1
0
0
1
1
1
1
0
-1
1
0
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0
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Sobel operator: It detects the edges along horizontal-axis
(180o) and Vertical-axis (90o). Sobel depends upon convolving
with an integer valued filter.
ISSN: 2231-5381
, here f is the noise intensity and
is the
standard deviation of white noise then generate initial brain
mask which contains the three-steps (i)Nonlinear anisotropic
diffusion (ii)automatic threshold (iii) Mask refinement. In third
step, we refine the mask which is using the “snakes” algorithm
and it is defined as determining the boundary between the brain
and the intracranial cavity. It makes formless contours through
the initial brain mask’s perimeter to lock onto edge of the brain
[7].A particular active contour is a collection of n points as:
= { 1, … … … … . ,
=( ,
Canny Edge detector: Canny edge detector is a multistage
process. It detects a wide range of edges in images. In this, we
use Gaussian convolution to smoothen the image and then
highlight the regions of image using first derivative.
−
}
(1)
) = {1, … … … . . , }
(2)
Here each point vi, an energy matrix E (vi) is expressed as:
E(vi)=
(3)
( )+
( )+
( )+
( )
( ) is continuity,
( ) is balloon
Here,
( ) is an intensity energy force,
( ) is a
force,
gradient energy function. Each vi moved to the point where
minimum energy in its neighborhood which is the smallest
element in E (vi).
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International Journal of Engineering Trends and Technology (IJETT) – Volume 12 Number 8 - Jun 2014
Fig.4(c)
Fig.4 (d)
Fig.3.Refinement of intracranial contour (a) The contour defined
by the perimeter of initial brain mask. (b) The intracranial
contour detected using the active contour model algorithm.
(B)Ant colony optimization: ACO-based image edge detection
defined as a number of ants to move for making a pheromone
matrix. The movement of ants goes in the direction that you
wanted by the local variation of intensity values in the image. In
the proposed algorithm, we start from the initialization process
then N iterations to construct the pheromone matrix through
construction process and the last one is the decision process
which determines the edge. We are using the following four
functions and are expressed as [8]:
F(x) = if x>=0
(4)
F(x) = x 2 if x>=0
(5)
F(x) =
sin
0
F(x) =
sin
0
else
(6)
0=<x=<
else
(7)
Here, is a parameter, figure (a), (b), (c), (d), (e) present a
different results of image cameraman and it show as:
Fig.4 (a)
Fig.4.(a)Original
Image
function’s equation (4)
function’s equation (5)
function’s equation (6)
function’s equation (7)
(b)Proposed
ACO
(c) Proposed ACO
(d) Proposed ACO
(e) Proposed ACO
An ACO and Snakes contour based edge detection has been
successfully developed. Segmentation of MRI brain images help
in the accurate detection of the region of interest. In the
proposed approach, both provide a superior performance [5].
Snakes contour detects the intracranial boundary which has
proven on research MRI data sets provided from various
scanners. In ACO is performed as to refine the edges
information( i.e. extracted by ACO) [5].Furthermore for future
research work, the parallel Ant colony optimization algorithm
can be reduce the computational load of this
experiment[2].Automatic brain segmentation to the problem of
lung segmentation for lung diseases(cystic fibrosis and
emphysema where we needed the volume of the lungs. Early
results with MR images are promising and can be continued.
Fig.4 (b)
ISSN: 2231-5381
with
with
with
with
V. CONCLUSION AND FUTURE WORK
0=<x=<
Fig.4 (e)
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International Journal of Engineering Trends and Technology (IJETT) – Volume 12 Number 8 - Jun 2014
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