LIVER TUMOR CLASSIFICATION USING SVM AND ANN TECHNIQUE
FOR HEALTHCARE
K.RADHA1 and S.SELVARAJAN2
1
Mahendra Engineering College, Namakkal-637503, Tamilnadu, India
e-mail:[email protected]
2
Muthayammal College of Engineering, Rasipuram -637408, Tamilnadu, India
Abstract
The classification of liver tumor using support vector machine and Artificial Neural Network classification
technique is presented in this paper. The preprocessing was done using median filter and liver was segmented from
CT images using region based segmentation. The features are extracted GLCM and Tamura from the segmented
liver region. The numerical and textural information were obtained from the extracted features. The features such as
Angular second moment, Contrast, mean, Correlation and entropy of the obtained sub bands were calculated. The
extracted features were optimized using the particle swarm optimization and fed as input to classification. The
classification results are compared with the other existing techniques. It is seen that comparative the classification
SVM and ANN give the better results.
Key words Region based segmentation, GLCM, Tamura, Particle Swarm Optimization, ANN and SVM.
Introduction
Liver cancer is found to be one of the leading cause for worldwide in the recent years. M. M EL-Gendyet. al [1]
presented the histogram equalization is used to increase the quality and contrast of images. Throughout feature
extraction step, some set of features are extracted using statistical, intensity, morphological, frequency domain and
wavelet domain based features. PCA is used for feature selection.
Y. W. Chenet. al [2] have reported that initial image was selected and segmented automatically. Intensity mean
and standard deviation of the image are selected as prior knowledge. Based on this prior knowledge, to find the liver
(object) and non-liver(background) areas using K-means clustering. Graph cut is working to segment liver region
from these detected areas. J. Lu, D. Wang et. al [3] develop the wavelet transform was used to make input to support
vector machine which classified pixels in liver and non-liver region. SVM classification is not smooth after
segmenting. For improvement of results authors used region growing method. Dilation method used for refining
holes and broken areas within liver. Using erosion method misclassified pixels outside liver are removed.
S.S. Kumar, et. al [4] develop the liver tumor segmentation using FCM clustering method is not efficient with
noise or out of the points and with clusters of different volume and unequal sample sizes. To overcome these
troubles, an alternative FCM clustering algorithm is used. another Fuzzy C Means (AFCM) is a segmentation
algorithm that is based on clustering related pixels in an iterative way, where the cluster centers are adjusted for all
iterations. Pavlidis , et. al Completed in their papers that automatic image processing in an Efficient image
segmentation is one of the majority critical tasks in [5][6].
Liu Jian-hua et. al, have reported that segment CT images of liver cancer effectively. Based on Snake model a
new method was proposed. Both threshold and snake model image segmentation was combined and a contour
correction process. It has been used to overcome optimization problems [7].K.Mala et. al discussed the general
CAD system for liver diseases. The first step is the extraction of the liver image from CT image. The second step is
the proper feature extraction from the liver image to describe the different liver tissues. The third step is the
classification of the liver diseases [8].
Suhuai Luo, et. al [9] presented the liver segmentation methods are categorized into three main classes
including gray level, structure and texture based method. In each method, the latest advance is reviewed with outline
comments on the merits and demerits of each discussed approach. Performance comparisons among the methods are
given along with the remarks on the problems existed and possible solutions. R. Suganya, et. al [10] introduce the
detection of liver disease from ultrasound images using Hybrid Kohonen Self Organizing Map.The diagnosis
method includes three steps: noise reduction, extraction and classification. The Hybrid Kohonen Self organizing
map is used to classify normal and abnormal liver diseases from ultrasound images.
D. Selvathi, et. al[11] presented the hybrid segmentation algorithm using Contour and change the Extreme
Learning Machine in the differential analysis of liver tumors in images are proposed. The liver is segmented using
adaptive threshold technique and morphological processing. Extraction of liver tumor is done by means of Fuzzy C
Means (FCM) clustering. The statistical and textural information are obtained from the extracted tumor using
Contour let Transform. The classification technique differentiates the tumor with comparatively high accuracy and
provides a second opinion to the radiologist. Z. Yuan et. al[12] develop an improved fuzzy cluster technique,
combining with a multiple cycle processing, and the last based on the segmented results, the liver is visualized by
Marching Cube method.
S. Sangewar et. al[13] have reported that segmentation based on a modified k-means segmentation method with
a special localized contouring algorithm. The advantage of this method provides potential and fast accurate liver
segmentation and 3-D rendering besides indicate the exact position of tumor region(s), all with minimal user
interaction. Evgin Goceri , et. al[14] develope an algorithm for fully automatic liver segmentation. This algorithm
avoids solving partial differential equations and applies only integer operations with a two-series segmentation
algorithm. The segmented results are evaluated with four different similarity measures.
Weimin Huang ,et. al[15] develop the tumor segmentation method in three-dimensional space, either a tumor
image or normal image. A fast learning technique Extreme Learning Machine (ELM) is trained as a voxel classifier.
ELM can be classified with only healthy liver samples in training. Compare it with two-class ELM and to extract the
boundary by randomly selecting samples in 3D space in a particular limited region of interest (ROI).
Saima Rathore, et. al[16] have reported that texture is a combination of repeated patterns with regular/irregular
frequency. The difficulty of liver texture was solved by encoding it in terms of positive parameters for texture
analysis. Arne ,et. al[17] develop an algorithm for probabilistic boosting tree to classify the liver as providing both
detection and segmentation of the lesions at constant time and fully automatically
C.M.Li, et al[18] presented the regularized a level set technique for solve the problem of additional general
&efficient low-level formatting of the extent set operate. They proposed a level set operate and comparatively giant
time steps are often efficient. The author difficult to notice a finite difference theme to cut back the amount of
iterations. Generally, the combine classification method has high accuracy and it has very small variation in the
accuracy compared with other classification methods. Keeping the above facts, the classification of liver tumors to
identify the stages of cancer has been developed and presented in this paper.
Implementation of proposed system
The flow diagram of the proposed liver tumor classification system is shown in figure1.The liver tumor has
been extracted and processed using different kind’s techniques. The features obtained are expected to provide
valuable information to analyze the nature of liver tumor for further clinical pathology.
Image Preprocessing
The liver tumor image can be obtained by the computed tomography systems DICOM files. In the
development of liver tumor classification system, the analysis of liver tumor detection depends on the regions,
which are usually of noise and low contrast. For this reason an image preprocessing and enhancement possibly
required to maintain the image quality, image features and suppressing the noise. Non –liner filter such as median
filter has been used preprocessing. This filter is applied to remove the noise and enhance the edges using Contrastlimited adaptive histogram equalization (CLAHE). The texture and region of abnormal images are found to be the
most important features in liver tumor classification. Preprocessing could be helpful for improving feature extraction
and selection of benign and malignant tumor []. This can be seen from the figure 2.
𝑓(𝑥, 𝑦) = 𝑚𝑒𝑑𝑖𝑎𝑛(𝑠, 𝑡). 𝑆𝑥𝑦{𝑔(𝑠, 𝑡)}
De noising
by median
filter
Fig.2 Output of the preprocessed image
SEGMENTATION
Segmentation is a process of dividing the digital images into multiple segments. Normally, the scanned
images obtain multiple images with some background on single scene which affects the accuracy of liver tumor
classification. To avoid this, multiple scanned images are divided into segments from which the background images
are eliminated. Most of the segmentation techniques use either edge information or region information. This
information is used to create an energy function. This function behaves like a fitness function to prefer the connected
regions having same label and the region growing is used to understand the optimized values. But, the segmentation
techniques with highest accuracy use both region and boundary based information (Boykov et al 2001). Here, the
region based segmentation technique uses this information for segmentation. The region growing can be processed
within the following steps:
Input Image
Preprocessing Using Median
Filter
Image Enhancement using
CLAHE
Region based Segmentation
FEATURE EXTRACTION


Gray Level Co-occurrence
Matrix
Tamura
Optimization of Features using
Particle Swarm Optimization
Support Vector
Machine with
Artificial Neural
Network Classifier
Liver Tumor
Classification
Feature Subset
Classification System
Benign
Malignant
Figure 1.flow diagram of liver tumor classification system
i.
Group of seed pixels are selected from original image
ii.
A set of similarity criterion such as grey level intensity or color are selected and then set up a stopping rule
iii.
Based on the properties of seed pixels, neighboring pixels are predefined by growing regions appending to
each seed
iv.
When there are no other pixels left by not satisfying the criterion then stop the growing region
The output of the segmentation is shown in figure 3.
a) Contrast Enhancement Image
b) Segmented Image
Figure 3:Segmented Output
The third stage of the proposed classification system focuses on the extraction of features and selection of
optimal features .The texture features are extracted from the region based segmentation image. The optimal features
are selected using Particle Swarm Optimization (PSO) technique.
Feature extraction method
Gray Level Co-occurrence Matrix (GLCM) These features provide different statistical measures of an image. In
this study the features such as Angular second moment (ASM), Contrast, Entropy, Variance , correlation, Sum
Entropy, Difference Variance, Inverse Difference Moment (IDM), Sum Average, Sum Variance, Difference
Entropy, Information Measure of Correlation feature 1 and Information Measure of Correlation feature 2 are
considered.
The co-occurrence matrix C is defined over an array of ‘n X m’ of an image I. It is parameterized by an offset (Δx,
Δy) as follows:
n m 1
C x, y(i, j )    
p1q10
if I ( p, q)  i and I ( p  x, q  y) 
otherwise
Where,
i and j
-
image intensity values of an image,
p and q
-
spatial positions in an image I
j


(1)
Δx, Δy
-
offsets, that depend on the direction (θ) and the distance (d) at which the matrix is
computed.
Here, the concentration is mainly on GLCM based texture extraction. GLCM is one of the earliest
methods for feature extraction. It contains the information about spatial relationship of pixels in an image. This
spatial information is represented in the form of second order statistical moment. A small 4X4 sub images with 4
gray levels and the corresponding GLCM P (i, j/ Δx=1, Δy=0) are shown in Figure 3.2.
Figure .4 GLCM and Normalized GLCM for 4X4 image
The GLCM is computed using the probability distribution P (d, θ) of clustering image. Here, θ takes
the values of 0o, 45o, 90o and 135o. Pt (d, θ) represents the transpose of P (d, θ). The following represents the
transpose of P (d, θ):

P (d, 00) = Pt (d, 1800)

P (d, 450) = Pt (d, 2250)

P (d, 900) = Pt (d, 2700)

P (d, 1350) = Pt (d, 3150)
Tamura these features are estimated based on Contrast, Directionality, Coarseness, Line likeness, Regularity and
Roughness. The contrast measure is defined as follows:
Contrast 

, n  1/ 4
n
 4 
(2)
Where,4 is given as follows:
4 
4
4
 
4 - Fourth moment about the mean and 2 - Variance
(3)
Equations 2 and 3 are used to estimate the contrast feature of an image.
Directionality is another measure to find out the total degree of directionality. The angle and magnitude are
calculated for each pixel in an image. Here, the first step is to calculate the edge direction
  tan 1
V 

H 2
(4)
Where, ∆H, ∆V - Horizontal and vertical derivatives,
Coarseness is used to define the differences between coarse and fine textures. It takes the average at every point over
neighbourhoods in the linear size of powers of 2. The average size of the neighbourhood 2 kx2k at each point (x, y) is
represented as follows.
Coarseness 
1 n n k
  2 p(i, j )
n2 i  1 j  1
(5)
Line likeness refers to the shape of texture primitives.
n n
2 

  pdir (i, j )Cos (i  j ) 
n 

i 1 j 1
LineLikeliness 
n n
  pdir (i, j )
i 1 j 1
(6)
Where, pdir(i,j) is the ‘nxn’ local directions of a point d.
Regularity refers to the variations in texture primitives.
Re gularity  1  n(
Coarseness
Where, n – Normalized Factor and

Contrast

directionality

linelikeliness
)
(7)
 - Standard Deviation
Roughness refers to the tactile variations of a physical structure. The following Equation shows the computation of
roughness.
Roughness = Coarseness + Contrast
(8)
Optimal feature selection using Particle Swarm Optimization algorithm
The numbers of texture features extracted are more numbers and the optimization of feature set has
become essential. This can be achieved using Particle Swarm Optimization technique. The output of the
optimization is expected to provide the optimal set of features which can be used as input the classifier. The
optimization algorithm is expected to minimize the number of redundant features and minimize the error rate.
In PSO, the candidate sets are generated using fitness functions. As the user randomly initializes the
features for candidate set, the fitness function consumes more time for processing and some of the best features may
not be considered from the candidate sets. To overcome these problems, this paper introduces the ranking based
fitness function to evaluate the candidate sets.
In this algorithm each particle coordinates are in the form of problem space. This problem space is
stored with its best solution achieved using fitness function. This fitness value is called as ‘pbest’. The next best
value is tracked by the PSO using its neighborhood particles. The location of next best value is called as ‘lbest’. PSO
considers all the ‘lbest’ and finally computes the global best called as ‘gbest’. PSO starts with random initialization
and the swarm moves in the search space to find out best solution by updating the position of each particle using its
own experience and neighboring particles. During each movement of the particle, velocity is altered towards the
‘pbest’ and ‘lbest’.
Fitness function for Ranking based PSO
During feature selection, combination of feature values produces better accuracy in classification. The
combined feature subset is generated using fitness function. This function gives accurate features subset for
classification. It is defined in terms of scatter index or class separation, which provides expected fitness for the
selected feature subset. The fitness function is represented as P = F 1F2F3....FN, where N = 1, 2, 3...., m. The
parameter ‘P’ represents a particle, which belongs to the fitness function of each feature subset. Value ‘1’ indicates
the selection of feature subset and ‘0’ indicates the rejection of feature subset. Maximum accuracy is absorbed using
the fitness function of the selected feature subset.The values of M 1, M2...ML and M0 represent the mean of
corresponding classes and the grand mean of the feature space. The mean (M i) can be calculated as follows:
Mi 
1 N
 M , i  0,1, 2,3,..., L
N i1 i
(9)
Where, Cji is the sample images in the class Cj. The grand mean (M0, M1 ... MN) is calculated from the weight factor
as follows:
N
 M 
i 1
i
1 L
 W F , i  1, 2,3,..., L
N i1 i i
i
Where, Fi – Features of an image and Wi – Weight factor
(10)
The scatter function or class separate function ‘F’ is computed using the following fitness function.
F
L
t
 (M i  M 0 ) (M i  M 0 )
i1
(11)
Selection of Optimized Features
Here, the position of a particle is represented as P i = (Pi1, Pi2... PiN) and the position change between
the current and next position is called as velocity. Velocity of a particle is denoted as V i= (Vi1, Vi2...ViN}. The fitness
function is calculated for each particle in the swarm and it is compared with previous best particle ‘p_best’. The
optimized new particle is called as ‘g_best’. After finding these two values, the particle’s position is changed from
its current position. The velocity of a particle can be calculated using the following formula:
V t  1  w *V t  c * rand1( Pbest  P t )  c * rand 2*( gbest  P t )
i
i
1
i
2
i
P t  1  P t  V t  1, i  1, 2,..., N
i
i
i
(12)
(13)
Where, c1 and c2 – Cognitive and Social parameters
Classification of tumor
The artificial neural network algorithm is widely used for neural network concept. The artificial neural network
shown figure 5 can be considered for classification of tumor. The obtained optimal features can be used to classify
the tumor into benign and malignant.
Depending on these values the classification of tumor is done using SVM [4]. The SVM produces a model, which
predicts the target values of the test data given only the test data attributes [13]. The testing data contains all the
images loaded onto the database. The training data contains two categories: ‘01’ for benign and ‘02’ for malignant.
The SVM and ANN are the most classifiers having smart performance and a lot of potency compare to different
classifiers. In this paper gives the combined SVM with ANN for tumor classification. Normally, ANN classifier
having less parameter compared to SVM and SVM is not plagued by the noise sensitivity. Here, the kernel function
is used to enhance the performance of the SVM. The back propagation algorithm plan is framed so as to cut back the
error until the ANN learns [8]. The training method starts with the random weights wherever as the goal is to
regulate them so as to cut back or minimize the errors. The error depends on the output, input, and weights are
calculated. Then adjust the weight mistreatment the strategy of gradient descendent. The following steps involved in
classification.
Step 1: Get the image
Step 2: Preprocessing method is applied to the image
Step 3: Find the texture using region based segmentation
Step 4: Find the feature
Step 5: Apply the combine classification technique for tumor
Result and Discussion
The images from DICOM Library have been considered for developing classification system. The proposed system
features were extracted for further analysis using MATLAB. The sample images have been preprocessed using
Median filter. An experiment has been conducted on a CT scan Liver image data set based on the proposed flow
diagram as shown in Figure 1. The figure 4 shows the original input image and Figure 5 represent the result of
preprocessed image of original image, is used to reduce the noise removal at the scanning phase.
Figure 4:Input Image
Figure 5:Noise reduced Image
Median filter technique has been used to reduce the noise in the CT scan liver image. Using this filter speckle
noises are also removed. Fig 4 shows Histogram Equalization of filtered CT scan liver image. Contrast Limited
Adaptive Histogram Equalization (CLAHE) for image enhancement. CLAHE images are transformed from grey
level images to binary images so that clear feature can be produced with background instead of pixel 0 and
foreground representing with pixel 1.
Figure 6:Enhanced Image
Figure 7:segmented image
This technique can also be used to find specific shapes in an image. Region based segmentation image is
shown in Fig 7. For the object segregation, the edge features have been used. It has been done using contour
tracking. Fig7shows the segmented portion of an image. It represents the extracted image by merging the segmented
portion with the input of CT scan liver image. From the extracted image the tumors are extracted Texture features
have been calculated for all segmented objects using Particle swarm optimization stored in the transactional
database.
From the transactional database combine Classification Technique has been generated using the
ANN with SVM algorithm, which is the combined approach to classify the CT liver images. The results show that
the proposed method can have better accuracy than the existing classification method. Depending on the hybrid
classification algorithm the diagnosis can be made by both the physicians and the proposed system. The proposed
system gives better results than the single classifier methods like ANN and SVM; it is described in the table 2 and 3.
In diagnosing liver tumor CT has become major imaging modalities. Different authors have used totally
different techniques for classification of liver growth from CT. The performance of the proposed method has been
evaluated in terms of accuracy, sensitivity and specificity. Sensitivity (Relevant Features) it means how many liver
tumors are accepted in the outcome compare with other. Specificity (Irrelevant Features) it shows how many nonliver tumors are rejected in the outcome. Overall Accuracy is employed to the final performance and also the
sensitivity is used to the acceptance capability. Accuracy is most commonly used metrics to performance of
classification. The accuracy of a classifier depends on the degree to which classifying rules square measure true. The
effectiveness of the proposed method has been estimated using the following measures:
𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
RF + IF
RF + IF + IFF + INR
Where RF is the number of relevant features; IF is irrelevant features; IFF is irrelevant false features; INR
is irrelevant not required.
𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 =
RF
RF + INR
𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 =
IF
IF + IFF
Table 1 Performance of the Classifiers
Classifier type
Time in ms
Multilayer Perceptron
Classifier Accuracy (%)
17.93
87.24
K Nearest Neighbor
10.5
84.68
J48graft
9.3
88.91
FUZZY
8.3
90.23
Support Vector Machine
7.0
96.38
Artificial Neural Networks
6.1
97.11
Combined algorithm
2.01
98.22
Table 2: Performance Comparison of Sample data set 1
S.no
Classifier
Relevant
Irrelevant
Features
Features
(RF)
(IF)
Irrelevant
Irrelevant
Negative
False
Not
Sensitivity
Specificity
Past predict
Predict
Features
Required
(%)
(%)
value(PPV)
Value
(IFF)
(INR)
(NPV)
accuracy
(%)
1
Multilayer
0.92
1
0.975
0.393
70.06
49.36
47.91
71.27
87.24
2
KNN
0.97
1.02
0.843
0.265
78.54
45.24
48.74
76.08
84.68
3
J48graft
1.07
0.75
0.877
0.352
75.24
53.90
58.79
71.35
88.91
4
FUZZY
0.95
0.98
0.951
0.237
80.03
49.24
49.22
80.05
90.23
5
ANN
1.18
0.90
1.098
0.109
91.54
54.95
56.73
90.96
96.38
6
SVM
1.25
0.77
1.322
0.069
94.76
63.19
61.88
95.03
97.11
1.35
0.70
1.456
0.025
95.07
67.53
65.85
98.31
98.22
7
Combined
algorithm
Table 2 and Table 3 provide the classification results and its performance with the existing classifiers. The
proposed method gives better results as compared with the existing methods with respect to sensitivity and
specificity and accuracy. The obtained results are shown in Table 2 and Table 3. Accuracy is used to approximate
how effective the classifier is by showing the percentage. In this case, the accuracy of hybrid classifier is better than
other. Thus, it means that hybrid classifier could correctly classify more data than other classifier. Sensitivity and
specificity assess the effectiveness of the classifier.
Table 3: Performance Comparison of Sample data set 2
S.no
Classifier
Relevant
Irrelevant
Features
Features
(RF)
(IF)
Irrelevant
Irrelevant
Negative
False
Not
Sensitivity
Specificity
Past predict
Predict
Features
Required
(%)
(%)
value(PPV)
Value
(IFF)
(INR)
(NPV)
accuracy
(%)
1
Multilayer
0.94
1.03
0.899
0.373
71.59
46.60
47.71
70.67
83.13
2
KNN
0.99
0.96
0.952
0.309
76.21
49.79
50.76
75.49
86.31
3
J48graft
1.04
0.84
0.976
0.287
78.37
59.65
55.31
77.27
87.53
4
FUZZY
0.89
0.95
0.873
0.257
77.59
47.88
48.36
77.25
87.27
5
ANN
1.19
0.95
1.134
0.129
90.21
54.41
55.60
89.78
94.74
6
SVM
1.28
0.86
1.345
0.096
93.02
60.74
57.39
93.33
96.47
1.302
0.73
1.396
0.037
97.23
65.66
64.07
97. 41
7
Combined
algorithm
98.64
As state earlier, sensitivity measured the performance of the classifier on the amount of correctly classified
benign tumors while specificity examined the performance of the classifiers on the amount of correctly classified
malignant tumors. Hybrid achieved higher value of sensitivity compared to other. Since the purpose of the cancer
detection is to detect whether the patient has cancer or not, which is represented by the existing of the malignant
tumors; the highest precision in specificity is more important in this research. Because patients that are detected as
cancer can be further investigated to prolong their survival but patients that are classified as normal will remain
undetected.
Conclusion
Liver tumor detection is an important technique within the medical vision for detection and classifying the liver
tumor. In this research, most of the previous researchers are using the SVM and ANN for detection the liver tumor.
The review is planned in a new hybrid method of categorizing a classification in step with the image feature it works
on, for that reason better summarizing the performance of each class and leading to find an optimum answer for
explicit classification. Performance comparisons table of the classification techniques and given possible solution. In
conclusion we imply that liver classification remains open issue and also the tendency is that multiple strategies are
utilized along to attain higher results.
COMPLIANCE WITH ETHICAL STANDARDS:
The authors declare that this paper has no conflict of interest. It is also declared that this article does not contain
any studies with human participants or animals The images available in the data set only used for the
analysis.
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