Computer Aided System for Detection and Classification …wireilla.com/papers/ijitca/V2N4/2412ijitca08.pdf · Computer Aided System for Detection and ... An intelligent computer-aided

International Journal of Information Technology, Control and Automation (IJITCA) Vol.2, No.4, October 2012

DOI:10.5121/ijitca.2012.2408 87

Computer Aided System for Detection and

Classification of Breast Cancer

S. Shanthi

1, and V. Murali Bhaskaran

2

1Kongu Engineering College, Erode, Tamil Nadu, India

[email protected] 2Paavai College of Engineering, Namakkal, Tamil Nadu, India

[email protected]

ABSTRACT

Breast

cancer is one of the most important causes of death among

all type of cancers for grown-up and

older women, mainly in developed countries, and its rate is rising. Since the cause of

this disease is not yet

known, early detection is the best way to decrease the breast cancer mortality. At present, early

detection of

breast cancer is attained by means of mammography. An intelligent computer-aided diagnosis system can

be very helpful for radiologist in detecting and diagnosing cancerous cell patterns earlier and faster than

typical screening programs. This paper proposes a computer aided system for automatic detection and

classification of breast cancer in mammogram images. Intuitionistic Fuzzy C-Means clustering technique

has been used to identify the suspicious region or the Region of Interest automatically. Then, the feature

data base is designed using histogram features, Gray Level Concurrence wavelet features and wavelet

energy features. Finally, the feature database is submitted to self-adaptive resource allocation network

classifier for classification of mammogram image as normal, benign or malignant. The proposed system is

verified with 322 mammograms from the Mammographic Image Analysis Society Database. The results

show that the proposed system produces better results.

KEYWORDS

Gray Level Concurrence wavelet features; self adaptive resource allocation network; Intuitionistic Fuzzy

C-Means Clustering; Mammogram; Wavelet Transformation;

1. INTRODUCTION

Breast cancer is one of the most significant health problems in the humankind, because it is the

major cause of fatality among all type of cancers for women in the 35 to 55 age group. Till now

there is no known way to prevent breast cancer but the earlier the cancer is detected, the higher

the chance of survival for patients. Mammography is one of the most successful methods that are

used in the early detection of breast cancer. In [1], the authors compared the efficiency of

mammography, Magnetic Resonance Imaging (MRI) and breast ultrasound for surveillance of

women at increased familial risk for breast cancer. Computer-Aided Diagnosis (CAD) systems

are aimed to help the radiologists in analyzing the mammographic images. The use of CAD in

medical decision support is now prevalent and pervasive across a wide range of medical

applications such as cancer research, kidney stone identification, heart diseases and so on. Now,

there is a tremendous opportunity for the use of data mining methods that assist the physician in

dealing with this flood of patient information and scientific knowledge. Several studies have

shown the potential of CAD in increasing the diagnostic accuracy [1-11]. Tang et al. [12]


88

conducted a study on the recent advances in the growth of CAD systems and its associated

techniques.

This paper proposes an integration of Intuitionistic Fuzzy C-Means (IFCM) clustering and Self

Adaptive Resource Allocation Network (SRAN) classifier approach for detecting and classifying

the breast cancer in mammogram images. This system is tested by using the images from

Mammographic Image Analysis Society (MIAS) datasets [13] and the results show high

precision, recall and F-measure.

2. RELATED WORK

The problem of mammogram interpretation in CAD can be divided into two sub-problems: the

first one is the detection and localization of the suspicious regions of the mammogram image and

the second, the most difficult sub-problem is defining the set of attributes to classify the identified

region into normal or benign or malignant. Both the problems have already been addressed by

various researchers. Oliver et al. [4], Shanthi and Murali Bhaskaran [9-10] discussed the

automatic detection and localization of the ROI. The different types of discriminating attributes

are defined in [2], [3], [5-8], [14].

In general, the medical image would have some amount of uncertain nature. To deal with

uncertainty Fuzzy C-Means (FCM) clustering and IFCM clustering have been used to segment

the ROI. Oliver et al. [4] applied FCM clustering algorithm to segment the image into two

clusters and considered both the clusters in feature extraction. Chaira [15] proposed IFCM

clustering method to cluster different regions of the Computed Tomography (CT) scan brain

images and these clusters may be used to identify the abnormalities in the brain. Shanthi and

Murali Bhaskaran [9] used IFCM clustering to detect the ROI from the mammogram image.

To enhance features of all sizes simultaneously, multiresolution enhancement methods, based on

the Wavelet Transformation (WT) have been developed. The resolution of a WT varies with a

scale parameter, decomposing an image into a set of frequency channels of constant bandwidth on

a logarithmic scale. This variation in resolution enables the WT to “zoom” into the irregularities

of an image and to characterize them locally. The hypothesis of any image wavelet analysis is that

the features of interest reside at certain scales. Specifically, features with sharp borders like

microcalcifications, mostly contain high-resolution levels (small scales) of the multiscale

representation. Larger objects with smooth borders, like masses, mostly contain low resolution

levels (coarse scales). WT and other multi-scale analysis have been used for computer image

representations in de-noising, compression and feature detection processing problems. Karahaliou

et al. [14] developed a system based on the WT.

Extracting the features from the image plays an significant role in image analysis and

understanding, with potential applications. The feature is defined as a function of one or more

dimensions, each of which specifies some proven property of an object, and is computed in such a

way that it quantifies some significant characteristics of the object. The following is a list of

statistical features identified in the literature that are used for classification: histogram features or

first order statistics [9-10], the second order features namely Surrounding Region Dependence

Matrix (SRDM) features [2], Gray Level Run-Length Matrix (GLRLM) features, Photometric

features [7], Wavelet Coefficient Texture features [9-10], [14], wavelet energy feature [9-10],

morphological features, Markovian texture features, Laws’ Texture Energy Measures (LTEMs)

[14], Gray Level Concurrence Matrix (GLCM) features [16].

Applications of machine learning techniques are becoming increasingly popular for classification

of medical data. Among various classification methods, neural network based classifiers have

been successfully used in a number of applications like medical image analysis [11], [17], [18].


89

Radial Basis Function Network (RBFN) has also been widely applied in many science and

engineering fields. An RBFN uses the radial basis functions as the activation functions of the

network and it has three layers namely input layer, a hidden layer and a linear output layer. The

activation function of the hidden layer in an RBFN uses Euclidean distance to compute the

distance between the inputs attributes and parameter vector of the network. Its training procedure

is usually divided into two stages: firstly, the centers and widths of the hidden layer are

determined by clustering algorithms and secondly, the weights connecting the hidden layer with

the output layer are determined by Singular Value Decomposition (SVD) or Least Mean Squared

(LMS) algorithms. In RBFN, selecting the appropriate number of basis functions remains a

critical issue. The complexity and the generalization ability of RBFN depends on the number of

basis functions in the network.

Suresh et al. [19] proposed a new sequential learning algorithm named Self-Adaptive Resource

Allocation Network (SRAN). This SRAN classifier uses RBFN as a basic building block and

problem independent self-regulated control parameters. This algorithm was applied for the

numerical data set taken from UCI machine learning repository. The data sets used in the work

are image segmentation (IS), vehicle classification (VC) and glass identification (GI). Shanthi and

Murali Bhaskaran [10] used SRAN classifier for mammogram classification in their previous

work.

The remaining parts of the paper are organized as follows: section 3 presents the proposed

system, section 4 presents experimental results and finally Section 5 concludes the proposed

system and discusses the future work.

3. PROPOSED WORK

The proposed work is an integration of Intuitionistic Fuzzy C-Means clustering and Self Adaptive

Resource Allocation Network classifier. All the mammogram images are preprocessed to remove

the noise such as image background, labels and pectoral muscle areas [20]. The data sets of the

proposed method are divided into a) training images and b) test images. Each training image is

related with a set of keywords. Algorithm 1 gives the outline of the proposed system.

Definition 1: Keywords are representative words that are chosen by a specialist for use in the

diagnosis of a medical image.


90

3.1. Remove the Noise

In a typical mammogram, several areas such as the image background, the tissue area,

informative marks and pectoral region and so on are present. In order to remove the noise, the

dilation and erosion operations are used. Dilation and erosion are basic morphological operations

defined in equations (1) and (2) respectively.

(1)

(2)

First, the image is converted into black and white. Then, sequence of morphological operations

dilation and erosion are applied for removing the label and background. Figure 1.a) and Figure

1.b) shows the original image and after removing artifacts respectively.

Figure 1.a) Original image-

mdb012

Figure 1.b) After removing artifacts and

background - mdb012

3.2. Remove the Pectoral Region

The removal of pectoral muscle region is necessary to increase the detection performance. A

window of size 7×7 pixels is extracted for each pixel centered at the pixel location. The median

value is computed for this window. The intensity value of the center pixel is replaced with the

median value. To remove the pectoral muscle region, initially the histogram is generated for the

mammogram image. The global optimum in the histogram is selected as the threshold value.

When the MLO view is correctly imaged, the pectoral region should always appear as a high-

intensity, triangular region across the upper posterior margin of the image. In many cases the

upper part of the boundary is a sharp intensity edge while the lower part is more likely to be a

texture edge, due to the fact that it is overlapped by fibro-glandular tissue. After finding the

global optimum value the image is scanned from top left to right (left breast) or top right to left

(right breast) in the triangular region across the upper posterior margin of the mammogram

image. The intensity values greater than this threshold are changed into black and the gray values


91

smaller than the threshold are maintain as it so as to convert the pictorial region as black region.

Figure 2.a) and Figure 2.c) shows the image after removing the pectoral region.

Figure 2.a) After

removing pectoral

region – mdb058

Figure 2 b) Original Image –

mdb001

Figure 2 c) After removing

background & pectoral region

– mdb001

3.3. Extraction of Region of Interest

The Intuitionistic Fuzzy Set (IFS) was defined as an extension of the ordinary Fuzzy Set [21]. As

opposed to a fuzzy set A in X is given by:

(3)

where µ A(x) →[0,1] is the membership function of the fuzzy set A.

Intuitionistic fuzzy set B in X is given by:

(4)

where µB(x) →[0,1] and νB(x) →[0,1] are such that:

(5)

and µB(x), νB(x)∈ [0,1] represent the degrees of membership and degrees of non-membership of

x∈ B, respectively.

For each intuitionistic fuzzy set B in X, ‘‘hesitation margin’’ (or ‘‘intuitionistic fuzzy index’’) of

x∈ B is given by:

(6)


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The equation (6) defines the hesitation degree of whether x belongs to B or not. It is obvious that

0≤πB(x)≤1, for each x∈X. Therefore, to describe an intuitionistic fuzzy set completely, any two

functions from the triplet must be used: membership function; non-membership function and

hesitation margin.

The fuzzy membership value and the cluster center are updated using the equations (7) and (8).

(7)

(8)

The objective function for IFCM clustering is

with m=2 (9)

In the proposed study each object has four properties namely pixel gray value, entropy,

mean, standard deviation, and pixel range. A small square window of 3 X 3 size is used

throughout the image to compute entropy, mean, standard deviation, and pixel range.

Figure 3. shows the segmented ROI using IFCM clustering.

Figure 3. ROI Using IFCM Clustering - mdb058


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3.4. Discrete Wavelet Decomposition

DWT transforms a two dimensional ROI image matrix into four subbands namely co-efficient

matrix (LL) and detailed co-efficient matrices: horizontal (HL), vertical (LH) and diagonal (HH)

respectively. Two level transformations are applied to the ROI as shown in Figure 4. For each

detailed coefficient subbands, 13 Gray Level Co-occurrence features and wavelet energy features

have been computed.

3.5. Feature Extraction

The thirteen features namely angular second moment or energy, contrast or inertia,

correlation, sum of square or variance, inverse different moment or homogeneity, sum

average, sum variance, sum entropy, entropy, difference entropy, difference variance,

information measure of correlation1 and information measure of correlation 2 are

extracted from the wavelet decomposed ROI [16]. Mean, standard deviation, gray level entropy,

kurtosis, skewness are extracted from the histogram equalized ROI. The extracted features

provide the characteristics of the input type to the classifier by considering the description of the

related properties of the image.

3.6. Self Adaptive Resource Allocation Network Classifier (SRAN)

Suresh et al. [19] recently proposed a sequential learning algorithm for multi category

classification with an in built self-regulated control mechanism. The SRAN classifier uses RBFN

as a basic building block . The most commonly used activation function in RBFN is the Gaussian

basis function. Suppose there are observation data {(x1,y1), (x2,y2), ….(xt,yt),..} where is

an m-dimensional features of observation t and is its coded class label. Here, n represents

the total number of classes and ‘n’ is 3 in the proposed system. If the feature observation x is

assigned to the class label c, then cth element of y=[ y1, y2 ...yc, …yn]T is 1 and other elements are -

1.

In the SRAN system, the training sample record arrives one by one and the network adapts its

parameters on the basis of the difference in knowledge between the network and the current

sample record. The maximum error E is calculated by using hinge loss function as defined in

equation (10).

E=maxi=1,2,3....n|ei| (10)

LL1 HL1

LH1 HH1

LL2 HL2

HL1

LH2 HH2

LH1 HH1

Figure 4. a) One Level DWT Figure 4. b) Two Level DWT


94

(11)

Where yi is the actual class label of ith sample and ýi is the predicted class label of i

th sample.

The output of this classifier is defined with k hidden neurons as defined in equation (12).

(12)

Where αij is the weight connecting the ith output neuron and j

th Gaussian neuron and Gaussian

basis function as defined in equation (13).

(13)

where , and ||_|| are the mean, standard deviation and the Euclidean norm respectively.

As each new sample is presented to the network, according to the sample error the self regulatory

system would perform any one of the following three actions.

1) The sample is used for

a) Network growing: if the predicted and actual class label is not the same and the error E is

greater than self regulated control parameter (ŋg), then the sample is to be used to add a new

hidden neuron in the network.

(14)

b) Network learning: if the predicted and actual class label is same and the error E is greater

than the self regulated learning control parameter (ŋl), then the network parameters are

updated.

(15)

The SRAN classifier uses the extended Kalman filter (EKF) to update the network parameters as

defined in equation (16).

W(t) = W(t-1)+KL(t)e (16)


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where KL(t) is the Kalman gain and e is the error obtained from hinge loss function. The self-

regulated control parameters are updated using the equations (17) and (18).

(17)

(18)

where δ is a parameter that controls the slope of the decrease of the control parameter which is

close to one.

2) If equations (14) and (15) are not satisfied then the sample is pushed to the rear end of the

stack for learning in future.

3) The sample is deleted from the dataset if the sample error E is less than 0.05 without being

used as a training sample to construct the network and thus it prevents over-training.

4. EXPERIMENTAL RESULTS

In order to test the proposed method, the widely known database MIAS has been used. The

database consists of 322 images which belong to three big categories: normal, benign and

malign. There are 208 normal, 63 benign and 51 malign images. The proposed system is tested

with 322 mammograms images from the MIAS database and it is implemented using MATLAB.

The classification performance of the proposed system is compared with that of other four

existing classifiers like RBFN [17], Multi Layer Perception (MLP), Navie Bayes and C4.5

classifiers [18]. The classification performance of all the four existing techniques are obtained

using Weka tool [22]. For each method of classification, the performance evaluation needs to be

calculated. In this, precision, recall and F-measure measures have been used to quantify the

efficiency of the proposed system in suggesting diagnoses of breast images.

(19)

(20)

Where

TP=>True Positive: A patient predicted with cancer when the patient actually has cancer.

TN=>True Negative: A patient predicted healthy when the patient actually healthy.

FP=>False Positive: A patient predicted with cancer when the patient actually healthy.

FN =>False Negative: A patient predicted healthy when the patient actually has cancer.


96

Precision score of 1.0 for a class C means that every item labelled as belonging to class C does

indeed belong to class C whereas a recall of 1.0 means that every item from class C was labelled

as belonging to class C.

A measure that combines precision and recall is the harmonic mean of precision and recall. The

traditional F-measure or balanced F-score is defined in equation (21).

(21)

The classification performance of RBFN, MLP, Navie Bayes, C4.5 classifiers and the proposed

system have been summarised in Table 1.

Table 1. Comparison of Existing Classifiers with Proposed System

It is clear that in terms of classification accuracy and other performance measures, the

propose system is superior to the other methods

.

5. CONCLUSION AND FUTURE DIRECTION

It is well known that mammogram interpretation is a very difficult task even for experienced

radiologists. In this system, a new approach for classification of mammogram image using Self


97

Adaptive Resource Allocation Network (SRAN) is demonstrated. First, the ROI is automatically

extracted using IFCM clustering. Then feature vector is constructed by extracting histogram

features, wavelet co-occurrence features and wavelet energy features. Then SRAN classifier is

used for classification.

Advantage of the proposed system is that samples without significant information are deleted

from the training set. This avoids over-training, reduces learning time, and minimizes the

computational effort.

In future, to improve the accuracy as well as the training time the optimal feature subset should be

identified. Most of the works in the literature show that the ROI is identified by using the data

given by the specialist. Automatically detecting the suspicious region is a challenge even today.

Instead of depending the specialist to identify the ROI, a fully automatic system is required in

future. Further testing has also to be performed with real databases in a clinical environment.

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Authors

Dr. V. Murali Bhaskaran received his B.E. Degree in Computer Science and Engineering

from Bharathidasan University, Trichy, India in the year 1989, M.E. degree in Computer

Science and Engineering and Ph. D in Computer Science and Engineering from Bharathiar

University, Coimbatore, India in the year 2000 and 2008 respectively. He has more than

23 years of experience in technical education. Now he is the Principal of Paavai College of

Engineering, Namakkal, India. He has published more than 30 papers in Journals and

Conferences both at National and International level. His area of interest includes

Cryptography and Network Security, High Speed Networks, Data Mining, Image

processing and Computer Architecture.

S.Shanthi received her B.Sc. Degree in Computer Science, MCA degree in Computer

Applications, MPhil degree in Computer Science from Bharathiar University, Coimbatore,

India in the year 1993, 1996 and 2004 respectively and ME degree in Computer Science

and Engineering from Anna University, Chennai, India in the year 2006. She is presently

working as an Assistant Professor (SLG) in the Department of Computer Applications,

Kongu Engineering College, Perundurai, India. Her area of interest includes, Data Mining,

Image processing, Pattern recognition and soft computing.

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Computer Aided System for Detection and Classification …wireilla.com/papers/ijitca/V2N4/2412ijitca08.pdf · Computer Aided System for Detection and ... An intelligent computer-aided