Abstract Microcalcification is one of the key symptoms facilitating early detection of breast cancer. In this paper image are acquisition from the mini mias database and perform various steps they are image enhancement, feature extraction and classification .In image enhancement the images are enhanced using filters and the features are extracted from the enhanced image using Gabor filter, and classify the micro calcification using support vector machine to classify the masses into benign, malignant, normal

1p G Scholar

C Kaviya Prabha

S Usha

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International Journal of Computer Trends and Technology (IJCTT) – volume 11 number 1 – May  2014 
ISSN: 2231-2803                      http://www.ijcttjournal.org               Page38 
 
Detection of Calcification Using Filter in 
Mammograms – A Survey 
                                                   C.Kaviya Prabha1,S.Usha2 
1P.G Scholar, Dept of Computer Application, Tamilnadu College of Engineering, Coimbatore,India. 
2 Professor, Dept. of  Computer Application, Tamilnadu College of Engineering, Coimbatore,India. 
Abstract  Microcalcification is one of the key symptoms 
facilitating early detection of breast cancer. In this 
paper image are acquisition from the mini mias 
database and perform various steps they are image 
enhancement, feature extraction and classification .In 
image enhancement the images are enhanced using 
filters and the features are extracted from the enhanced 
image using Gabor filter, and classify the micro 
calcification using support vector machine to classify 
the masses into benign, malignant, normal. 
Keywords—mammograms,imageenhancement, feature 
extraction, classification. 
I.  INTRODUCTION 
Breast cancer is one of the common cancers among 
women in this world. It is second biggest cause of 
death.Troughout 50,000women and 400 men are 
identifyingwith breast cancer each year. Breast 
cancer represents 19 % of death and 24% of other 
cancer in the European Community [1,2].The 
effective detection method of breast cancer is 
mammography. So the proposed method employs the 
textural patterns for robust and clear representation of 
masses. Here we employ histogram equalization for 
image enhancement .To enhances the image with 
contrast and brightness. While using of histogram 
equalization spreading most frequent intensities 
values effectively for feature extraction .Then apply 
enhanced image to the Gabor filter for feature 
extraction with different scales and orientation. The 
extracted features are classified as benign or 
malignant using support vector machine SVM for 
classification of the extracted image. To identify the 
mass as benign malignant or normal for early 
detection of breast cancer. This paper organized as 
follows in section 2 we present the proposed system.  
Subsequently section 3 we present comparative study 
of previous work. Finally section 3 is conclusion of 
this work. 
 
1. BREAST CANCER 
Breast cancer is one of the second leading causes 
cancer related death among women in the world,it 
develops from the breast cell. The death rate can be 
reduced from the early detection of cancer.The early 
detection of breast cancer which leads to the chance 
of survival. Screening mammography is the current 
method to detect the breast cancer earlier.The breast 
cancer starts from the inner linings of milk ducts or 
the lobules that carry the milk to a nipple. There are 
two types of breast cancer can start offnamely ductal 
carcinoma and lobule carcinoma. The cancer starts 
from the milk ducts are ductal carcinoma or the 
cancer starts from the lobules are lobule carcinoma. 
Most of the cancer starts from the ductal. 
The breast is like other part of the body it contains 
billions of micro cells. These cells can be multiplied 
in orderly fashion and news cell can be replaced by 
old dead cells. In cancer this order was goes wrong 
and the new cells are not need to the body and the old 
cells do not die these extra cells .these extra cells 
form a masses of tissue is called growth or tumor. 
The tumor can be classified two types they are benign 
International Journal of Computer Trends and Technology (IJCTT) – volume 11 number 1 – May  2014 
ISSN: 2231-2803                      http://www.ijcttjournal.org               Page39 
 
and malignant.Benign is normal masses and the 
malignant are cancerous. And the malignant tumor is 
spread to other parts of the body. 
The invasive and non invasive are the two types of 
breast cancer. The cancer cells are out from the 
lobules or milk ducts and spread to nearby tissues of 
the body is called invasive cancer. When the cancer 
is still inside its original place and has not broken out. 
Lobular carcinoma is in it’s inside the lobules, while 
ductal carcinoma is in its original place when they are 
still inside the milk ducts. Sometimes this type of 
cancers is called pre cancerous. 
2. MAMMOGRAPHY 
Mammography is a X-ray to inspect the human breast 
by using screening and diagnostic tool. It is the 
method to detect early stage of breast cancer.  The 
mammogram is a X-ray image of our breast on 
mammography. The screening mammogram is used 
to check the breast cancer having no signs and 
symptoms of diseases. It takes two views of each 
breast a craniocaudal (from above) and a mediolateral 
oblique (from the sides).After that the signs and 
symptoms are found means to test the breast cancer 
by using diagnostic mammogram and evaluate the 
abnormalities of the breast. It takes two or more 
views of each breast using angles, or special coned 
views of certain areas. During mammograms the 
breast is compressed into two plates. And X-ray pass 
through the breast and film plate is capture the image 
and displayed in the computer screen. 
3. DATABASE 
All the mammogram images are obtained from the 
mini MIAS (Mammographic Image analysis society) 
database. The images have 50 micron pixel edges is 
taken from the UK national breast 
screeningsprogrammed and the database has been 
reduced to a 200 micron pixel edges[20]. The 
database contains 322 mammogram images 
consisting of 208 normal, 63 benign, and 51 cancer 
images. These images were in 24-bit PGM format 
with image size is 1024x1024. 
II METHODS 
1.Image Enhancement 
The image enhancement is used to improve the image 
perception of information on the image for human 
viewers for the better inputs. The objective of the 
image enhancement is to modify the attribute of the 
image. In the image enhancement here we use the 
histogram equalization. It is used to improve the 
quality of the image and mainly used to reduce the 
extra brightness and darkness of the images.It 
produces a more in uniform manner distributed 
histogram so the image would become much clearer 
.The Input images are assigned by the intensity 
values of the pixels such that the output image had a 
same distribution of intensities. So the result of the 
histogram equalization is uniform and the contrast is 
increased from the images.  
 
2.Feature Extraction 
 
Feature extraction is the important step to detect the 
breast cancer. To obtain the feature extraction takes 
the image from the database and extracts the feature 
from that image. The performance of the system is 
used for the optimal parameter of the classifier is to 
separate the normal and abnormal tissue by using the 
feature vector. Themammograms contain texture 
patterns of different scales and orientations. These 
patterns are useful to separate from mass and non 
International Journal of Computer Trends and Technology (IJCTT) – volume 11 number 1 – May  2014 
ISSN: 2231-2803                      http://www.ijcttjournal.org               Page40 
 
mass. So here we use the Gabor filter technique used 
to extract the feature effectively. 
Since we extract texture features using Gabor filter, 
first a brief overview of Gabor filter is necessary for 
a deep understanding of the producer for feature 
extraction 
 
Gabor Filter 
The Gabor filter is the linear filter used for edge 
detection. It is represented by frequency and 
orientation that is similar to the human visual system 
and they have been found by appropriate for texture 
representation and discrimination. The Gabor filter is 
defined by sinusoidal waves and multiplied by a 
Gaussian function. The interesting property of the 
Gabor filter is optimal localization of both spatial and 
frequency domain[17].  
A filter will havestronger response to an edge where 
the normal are parallelto the orientation In order to 
extract textural patterns of mammograms, Gabor 
filters can be used with distinctorientations and 
scales, and thus provide a powerfultool for the 
representation of textural properties of masses at 
different scales and orientations. A Gabor filter bank 
contains multiple Gabor filters tunedwith different 
parameter settings (scaling, orientation andcentral 
frequency).  
 
3.CLASSIFICATION 
 
The method has the objective to classify each image 
of one of two classes the masses benign or malignant. 
Classifications have the assignmentto an unknown 
pattern of a predefined class, according to the pattern 
presented in theform of a feature vector. There are 
many classification techniques exist. After feature 
extractions of the mammogram images and classify 
the benign or malignant or normal it helps to predict 
the texture feature it plays a important role in 
classification. Here we use the technique for the 
classification is Support Vector Machine (SVM). 
 
Support Vector Machine (Svm) 
 
Support vector Machine is supervised learning 
models with associated learning algorithms that 
analyzes data and recognize patterns used for 
classification theregion. The SVM is one of the most 
recent methods designed to solve binary 
classification problems. It is a kind of large margin 
classifiers and it is vector based machine learning 
methods. It’s to be a part ofpositive and negatives 
examples from each other with maximum margin, 
which means, the distance between the decision 
surface and the closest example is maximised.The 
optimal hyper –plane is separated. and the data is 
belonging to various classes with large margins. To 
find a decision boundary of two classes that are 
maximally far from any point in  training data[14]. 
The distance between two classes is the sum of 
distance and it is defined as margin. The SVM is 
based on statistical learning theory and has attractive 
generalization capabilities in linear as well as non-
linear decision problems.  
 
III Overview Of The Previous Work 
 
 
 
METHODS 
 
 
REMARKS 
 
 
Gabor filter with adaptive 
statergy and user 
relvance feedback[4]. 
 
Used to extrct the texture 
feature and adaptive 
statergy is proposed for 
feature selection,filter 
selection and  future 
International Journal of Computer Trends and Technology (IJCTT) – volume 11 number 1 – May  2014 
ISSN: 2231-2803                      http://www.ijcttjournal.org               Page41 
 
weighting.This uses a 
users relevance feedback 
to reduce the redundancy. 
 
 
Sequential difference of 
Gaussian filter(DoG)[7]. 
 
 
To detect the 
microcalcification 
clusters.and compare the 
several microcalcification  
detect in each 
mammogram and all 
microcalcifications are in 
single cluster. 
 
Gabor Cancer Detection 
Alogorithm[3]. 
 
Used to test large sample 
of mammograms.To 
detect early stage masses 
and calcification. 
 
Region of Interest,Gabor 
filter Support Vector 
Machine[8]. 
 To extract local  spatial 
textural features from 
digital mammograms 
using different 
configuration , resolution 
and classify them as 
malignant 
,bengin,normal. 
 
Dual tree complex 
wavelet(DT-
CWT),Sationary Wavelet 
Transform and top- hat 
transform[9]. 
 
Helps provide a good 
performance due to more 
data phase 
information,shift 
varience,and 
directionality.and 
compared with other 
techniques. 
 
 
 
 
 
Gabor filter,Support Vector 
Mahine(SVM)[10]. 
 
Helps to address 
the multiscale and 
multi orientation 
textural properties 
of masses.And 10 
fold cross 
validation is used 
in classification 
achived 
Az=0.995,and the 
accuracy is 99.60. 
Dyadic wavelet,Fuzzy shell 
clustering.[5] 
The image quality 
enhancement 
using 
multirresolution 
Analysis to detect 
the MCs.The 
nodular 
components is 
used to achieve 
accurately 95% of 
the detection were 
correct. 
Roi, 
daubechies,symlets,biorthogonal, 
neural network.[6] 
Used to crop the 
image and analysis 
the texture using 
various filters.The 
combined feature 
vector isfed to the 
classification.And 
it is achieved 85% 
of over all 
classification. 
 
 
Ant Colony Optimization (ACO) 
and Genetic Algorithm 
(GA)[18]. 
Used to detect 
breast border and 
nipple.Cross over 
and mutation 
operators are used 
to generate mated 
string.To evaluate 
the performance 
by FROC curve 
and the result 
produced Az 
(0.94) 
Wavelet transform 
,Therasholding procedure[19]. 
Used to detect the 
microcalcification 
the detection 
directly is used by 
wavelet and no 
inverse 
transformation. 
 
IV CONCLUSION 
In this survey paper, method of detecting breast 
cancer by using mammogram image has been studied 
and discussed. This study is used to evaluate the 
mammogram contains micro calcification.This helps 
forfocusing the future develops in the field of 
medicalimage processing. Here detailed explanation 
aboutseveral techniques of medical image processing 
International Journal of Computer Trends and Technology (IJCTT) – volume 11 number 1 – May  2014 
ISSN: 2231-2803                      http://www.ijcttjournal.org               Page42 
 
inbreast cancer detection. This paper mainly 
dealswith the information about the various stages 
ofdetection such as image enhancement,feature 
extraction,and classification. 
 
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Detection of Calcification Using Filter in Mammograms -A Survey

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Detection of Calcification Using Filter in Mammograms -A Survey

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