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.

1. 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. Shanthi1
, and V. Murali Bhaskaran2
1
Kongu Engineering College, Erode, Tamil Nadu, India
shanthi.kongumca@gmail.com
2
Paavai College of Engineering, Namakkal, Tamil Nadu, India
murali66@gmail.com
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]

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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].

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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.

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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

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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

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(11)
Where yi is the actual class label of ith
sample and ýi is the predicted class label of ith
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 jth
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.

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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

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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.