Vidhyavinothini Radha Krishnan
, IJRIT 877
International Journal of Research in Information Technology (IJRIT)
www.ijrit.com www.ijrit.com www.ijrit.com
www.ijrit.com ISSN 2001-5569
Detection of Muscle Injuries using Image Segmentation Based on ABC Algorithm and Shearlet Transform
Vidhyavinothini Radha Krishnan1,Tamilselvan kumaravel Subramaniam 2, Aarthi Ramasamy 3 and Murugesan Govindasamy4
1PG Scholar, Department of ECE, Velalar College of Engineering and Technology, Anna University Chennai, Tamil Nadu, India
2Assistant Professor, Department of ECE, Velalar College of Engineering and Technology, Anna University
Chennai, Tamil Nadu, India [email protected]
3PG Scholar, Department of ECE, Velalar College of Engineering and Technology, Anna University Chennai, Tamil Nadu, India
4Professor & Head, Department of ECE, Kongu Engineering College, Anna University Chennai, Tamil Nadu, India
Abstract
Image segmentation is the process of partitioning a digital image into multiple segments. It is used to locate objects and a boundary in images. CAT (Computerized Axial Tomography) is an imaging method which is widely used in detecting tissue density. Among the many approaches the segmentation of CAT images, a popular method is Artificial Bee Colony (ABC) algorithm. In the proposed method, Shearlet transform is used to detect both the localization and orientation of the edges. The experimental results shows that the injury level determined by the proposed method can successfully help us to determine if the muscle is injured lot. The experimental result demonstrates that the proposed method is effective and efficient.
Keywords: Image Segmentation, Computerized Axial Tomography, Artificial Bee Colony algorithm, Shearlet Transform, Localization and Orientation.
1. Introduction
Segmentation is the process of image analysis in which the object of interest is located from the background. The result of image segmentation is a set of contours extracted from the image or a set of segments that are collectively covered the entire image. Each pixel in a region is similar with respect to some characteristics, such as intensity, texture or color. The ultimate aim of image processing is to extract the features of the image data. The segmentation of muscle injury from CAT images is important but time consuming task performed by medical experts and it is a huge challenge in image processing. The aim of this work is to segment the CAT image to detect the muscle scar. A computed Axial Tomography scan or
Vidhyavinothini Radha Krishnan
, IJRIT 878
CT scan is a test that provides very clear pictures of structures inside the body. CT scan helps in diagnosing various diseases. A CT scan uses X-ray technology and sophisticated computers to come up with the final pictures. CAT is an imaging modality that uses computer processing to generate a picture of the tissue density in a “Slice” as thin as1 to 10mm in thickness through the physical body. A newer CAT scanners use a high resolution matrix with 256*256 or 512*512 pixels. An imaging method [7] in which the cross- sectional image of the structures in a body plane is reconstructed by a computer program from the x-ray absorption of beams projected through the body in the image plane. Muscle is a soft tissue derived from the mesodermal layer of embryonic germ cell. Muscles are primarily powered by the oxidation of fats and carbohydrates, but anaerobic chemical reactions are also used, specifically by fast twitch fibers. These chemical reaction produce adenosine tri-phosphate molecules which are used to power the movement of the myosin heads muscle cell contain protein filaments, producing a contraction that changes both the length and shape of the cell. Muscle fiber types are histochemical, biochemical or morphological characteristics. A basic understanding of muscle structure and physiology is necessary to understand the muscle fiber classification techniques. Among numerous algorithm proposed for muscle injury CAT segmentation, the popular one is ABC algorithm. ABC is an optimization algorithm inspired by the natural behavior of honey bees in the search process for the best food sources. The main drawback is its search space is limited by the initial solution. The one way to improve accuracy of the result is to use the Shearlet transform. Shearlet transform is used to seek values for the set of parameters, the scaling and the translation. This transform provide optimally sparse approximation, and to find error rates and data estimation from the noise. It is well adapted for representation of edges. In our proposed method in order to enhance the results of segmentation, the ABC optimization algorithm and Shearlet transform are segmented.
2. Artificial Bee Colony Algorithm
The artificial bee colony algorithm (ABC) is an optimization algorithm based on the intelligent foraging behavior of honey bee swarm, proposed by Karaboga in 2005. In the ABC model, the uycolony divides into three groups of bees:
• Employed bees
• Onlooker bees
• Scout bees
It is assumed that there is only one artificial employed bee for each food source. The number of employed bees in the colony is equal to the number of food sources located in the hive. Employed bees go to their food source and come back to beehive and dance on the area. The employed bee food source has been deserted becomes a scout and starts to search for finding a new food source. Employed bees dances are watched by onlooker bee and then the food sources are selected according to the dances. In ABC algorithm, the position of a food source represents a feasible solution to the optimization problem [4] and the nectar amount of a food source similar to the quality that is to be fitness of the related solution. The number of solutions in the population is equal to the employed bees. In the first step, a randomly distributed initial population is generated. After initialization, the population forces someone to repeat the cycles for every search process of all bees. An employed bee [3] produces a modification on the source position in her memory and produces a new food source position. Provided that the nectar amount of new one is higher when compared to the old source, the bee memorizes the new source position and forgets the old one. The position of food source will be in memory .Later all employed bees complete the search process they share the position of the sources information with the onlooker bee on the dance area. Each onlooker estimate the nectar information grasp from all employed bees and then chooses a food source related to the nectar amounts. The employed bee produce a modification on the source position in her memory then checks the nectar amount. If the nectar is higher than the old one, the bee memorizes the new position and forgets the previous one.
The ABC algorithm involves the following procedures:
• The employed bee requires food sources initially
Vidhyavinothini Radha Krishnan
, IJRIT 879
• REPEAT
• Each employed bee goes to a food source and determines a neighbor source, and evaluates its nectar amount and dances in the hive
• Each onlooker bee watches the dance of employed bees and chooses their sources depending on the dances, and then goes to the source. After choosing a neighbor around that, and she evaluates its nectar amount
• Abandoned food sources are determined and it can be replaced with the new food sources discovered by scouts
• The best food source found and then registered
• UNTIL (requirements are met)
The position of the ith food source is represented by, Si = (Si1, Si2, ……,SiD). Information is shared by the employed bees for returning to the hive, onlooker bees go to the region of food source explored by employed bees at Si based on probability Pi defined as
1
∑
=
=
FSk k i
fit pi fit
(1)
Where, FS is total number of Food Sources. The equation for fitness value fiti is calculated by
) ( 1 fit 1
is
i+ f
=
(2) Where f(Si) denotes the objective function. The onlooker finds its food source in the region of
( Sij Skj )
r
Sij + −
= *
S
new (3)
Where Snew is the new food source used well by onlooker and k is the solution in the neighborhood of i, is a random number in the range -1 to +1 and j is the dimension of the problem considered.
If the new fitness value is relatively better than the fitness value achieved, then the bee moves to the new food source, otherwise reserve old one. If all employed bees complete the process then the information is shared with onlookers. Each onlooker bee selects its food source according to the probability. Hence good food sources are well assist with onlookers. The entire bee will search for good food source for a particular number of limit or cycles.
3. Shearlet Transform
The Shearlet transform is unlike the traditional wavelet transform which does not possess the ability to detect directionality, it associated with two parameters, the scaling parameter a and the translation parameter t. Compared to curvelet they have the additional property of being defined over a uniform grid which will also turn out to be beneficial when we introduce the tree structure on the index set below.
Shearlet are built using the operations of anisotropic dilation, translation and shearing.
Let G be a subgroup of the group of 2 × 2 invertible matrices. The affine systems generated
ψ
∈L2( )
R2 by are the collections of functions:
( ) | det | 2 ( ( )), 2,
1
,t x M M x t t R M G
M = − ψ − ∈ ∈
ψ (4)
If any L2 (R2) can be recovered via the reproducing Formula,
Vidhyavinothini Radha Krishnan
, IJRIT 880
( )
,
u u
, M,td M dt
R G
t M
n
λ ψ
∫ ∫ ψ
=
(5)Where λ is a measure on G, then ψ is a continuous wavelet. The continuous wavelet transform is the mapping for
( )
M,t ∈G×R2,u→Wψu
( )
M,t = u,ψM,t (6)There are a variety of examples of wavelet transforms. The simplest case is when the matrices
M
have the formaI ,
wherea > 0
andI
is the identity matrix. In this situation, one obtains the isotropic continuous wavelet transform:
( ) a , t a
1u ( x ) ( a
1( x t )) dx
u W
R
∫ −
=
−ψ
−ψ (7) Where the dilation factor is the same for all coordinate directions. In fact, if a function u is smooth apart
from a discontinuity at point
x 0
, then the continuous wavelet transform isW ψ u ( t a , )
decays speedily as→ 0
a
,if t is nearx 0
.This property is to determine the set of points where u is not regular, and explain the ability of the continuous wavelet transform to detect edges.The isotropic continuous wavelet transform is unfit to provide additional information about the geometry of the set of singularities of
u
. In many situations, including edge detection, it is useful to not only identify the location of edges, to capture their geometrical properties, such as the edge orientation. It can be achieved by employing a non-isotropic version of the continuous wavelet transform called the continuous Shearlet transform. This is defined as mapping.
SH
ψu ( a , s , t ) = u , ψ
ast(8) Where
ψ
ast( ) x = | det M
as|
−21ψ ( M
as−1( x − t ) )and M
as =
for
a > o , s ∈ R , t ∈ R
2.Perceive thatM
as= B
sA
a,whereA
a=
andB
s=
.Hence each matrix
M
as is associated two distinct actions: an anisotropic dilation produced by the matrixA
a and a shearing produced by the non-expansive matrixB
s.Fig. 1 Frequency support of the horizontal Shearlet (left) and vertical Shearlet (right) for different values of a and s.
In the frequency domain
Vidhyavinothini Radha Krishnan
, IJRIT 881
( ) ˆ ( ) ˆ
ˆ
1 2 2 1
1 1 1 4 2
3
2 ,
1
−
= a e
− Πi ta a
−s
ast
ξ
ψ ξ ξ ψ ξ
ξ
ψ
ξ (9)and, thus, each function
ψ ˆ
ast is supported in the set( ) , 2 , | a
2 1 2
, 1 : 2
1 2 1
2 ,
1
− ≤
∪
− −
∈ s
a a a
a ξ
ξ ξ ξ
ξ
(10)Thus each Shearlet
ψ
ast has frequency support on a pair of trapezoids, at various scales, symmetric with respect to the origin and oriented along a line of slopes
. As a result, the Shearlet form a collection of well-localized waveforms at various scales a, orientations s and locationst
.The shearing variables
corresponds to the slope of the line of orientation of the Shearletψ ˆ
ast, rather than its angle with respect to theξ
1 axis. It follows that the Shearlet provide a nonuniform angular covering of the frequency plane when the variables
is discredited, and this can be a disadvantage for the numerical implementation of the Shearlet transform. To avoid this problem, the continuous Shearlet transform is modified as follows.In the definition of
SH
ψ, the values ofs
will be restricted to the interval [−1, 1]. Under this restriction, inthe frequency plane, the collection of Shearlet
ψ
ast will only cover the horizontal cone( )
{ :| | 1 }
1 2 2 ,
1
≤
ξ ξ ξ
ξ .
In the frequency plane they are obtained from the corresponding “horizontal” Shearletψ
ast through a rotation by π/2. The frequency supports of some representative horizontal and vertical Shearlet are illustrated in Fig.1. By combining the two Shearlet transforms, anyu ∈ L
2( ) R2 can be
reproduced with respect to the combination of vertical and horizontal Shearlet.
4. Proposed Technique and Methodology
The proposed method involves the below steps:
Step 1 : Input muscle fiber CAT image
Step 2 : Pre-Processing is used to enhance the quality of input image.
Step 3 : Enhanced input image using i) ABC Algorithm ii) Shearlet Transform Step 4 : Output - segmented Image
In the pre-processing step, the identification of unhealthy muscle fiber is easier by converting the input CAT image into grayscale image. The median filter is used to remove noise from the image. Finally an enhanced image is produced.Fig. 2 represents the pre-processing.
Fig. 2 Block diagram of pre-processing CAT
Image
Pre- Processing
Enhanced Image
Vidhyavinothini Radha Krishnan
, IJRIT 882
Fig. 3 Block diagram of processing
Fig. 3 shows the detection of, enhanced image is segmented to locate the unhealthy muscle fiber.
The different methods are used for segmentation are artificial bee colony algorithm and Shearlet transform.
The quality of the segmented image is determined using the following performance measures.
4.1 Peak Signal-to-Noise Ratio
The term peak signal-to-noise ratio is an expression for the ratio between the maximum possible value of a signal and the power of distorting noise that affects the quality of its representation. Because many signals have a very wide dynamic range, the PSNR is usually expressed in terms of the logarithmic decibel scale.The mathematical representation of the PSNR is as follows:
=
MSE
PSNR 20log10 MAX f (11)
4.2 Mean Square Error
The mean square error is one of many ways to quantify the difference between values implied by an estimator and the true values of the quantity being estimated. MSE measures the average of the squares of the "errors." The error is the amount by which the value implied by the estimator differs from the quantity to be estimated.
( ) ( ) i j g i j
mn f
MSE
mi n
j
|| , ,
1
10 1
0
−
= ∑ ∑
=− −=5. Simulation Results
Fig. 4 Original Input Image Fig. 5 Segmented Image using ABC Algorithm
Fig. 6 Segmented Image using Shearlet transform Segmented
Image Shearlet Transform ABC
Algorithm Enhanced
Image
Vidhyavinothini Radha Krishnan
, IJRIT 883
Fig. 7 Proposed method
Set of Computerized Axial Tomography Images
Segmentation Methods
Performance Measures Radiologist’s Opinion
PSNR MSE
ABC Algorithm Shearlet Transform Proposed Method
54.81 116.96 128.13
0.108 0.103 0.056
TP
ABC Algorithm Shearlet Transform Proposed Method
41.73 117.03 117.42
0.111 0.041 0.026
TP
ABC Algorithm Shearlet Transform Proposed Method
25.53 107.98 121.66
0.444 0.043 0.040
FN
ABC Algorithm Shearlet Transform Proposed Method
34.33 112.67 127.52
1.000 0.072 0.053
TP
ABC Algorithm Shearlet Transform Proposed Method
40.91 122.52 139.81
1.778 0.062 0.059
TP
ABC Algorithm Shearlet Transform Proposed Method
35.02 109.74 127.50
2.777 0.061 0.048
TP
Vidhyavinothini Radha Krishnan
, IJRIT 884
We quantify TP and FN as:
TP: True Positive, means region segmented as injured that proved to be injured.
FN: False Negative, means region segmented as not injured that proved to be injured.
TPR: True Positive Rate.
FN TP TPR TP
= +
6. Conclusions
The effectiveness of the proposed method for Image segmentation is evaluated by using various parameters such as PSNR and MSE. The proposed work is expertise with ABC algorithm and Shearlet transform. The different image segmentation techniques have been implemented for medical images and the results are compared. The proposed method will be highly effective for determining the injury level.
From the obtained results, the PSNR value is higher and MSE value is lower. Thus the proposed method outperforms over the existing segmentation techniques.
References
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[7] N.R.Pal and S.K.Pal, “A review on image segmentation techniques,” Pattern Recognition, Vol. 26, pp. 1277-1294,Mar 1993.
[8] S.Pillen, et al., “Skeletal muscle ultrasound: correlation between fibrous tissue and echo intensity,”
Ultrasound in Medicine & Biology, Vol. 35, issue 3, pp. 443-446,March 2009.
[9] S.Pillen, R.R.Scholten, M.J.Zwarts and A.Verrips , “Quantitative skeletal muscle ultrasonography in children with suspected neuromuscular disease,” Muscle & Nerve, Vol. 27, issue 6, pp.699-705,Apr 2003.
[10] Wei-lunchao, Yu-HsiangWanget.al,“ Muscle injury determination by image segmentation,” IEEE Transactions on Visual Communication and Image Processing, Nov 2011.
