Abstract: Mammography is an important research field. Mammography Image classification is an area of interest to most of the researchers today. The aim of this paper is to detect the Mammography image for its malignancy. Different methods can be used to detect the malignancy. This paper represents GLDM feature extraction method and SVM classifier. Experiments were conducted on MIAS database. The results show that combination of GLDM feature extractor with SVM classifier is found to give appropriate results

M R Khare

N V Jog

S R Mahadik

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ISSN: 2319-8753 
                 
International Journal of Innovative Research in Science,  
Engineering and Technology 
(An ISO 3297: 2007 Certified Organization) 
Vol. 2, Issue 12, December 2013 
Copyright to IJIRSET                                                      www.ijirset.com                                                                            7817 
 
Implementation of Classification Technique 
for Mammogram Image   
 N.V.Jog 
1
, S.R.Mahadik
 2
, M.R.Khare
3
 
Lecturer, Medical Electronics, G.P.Miraj / Shivaji, Miraj, Maharashtra, India
 1
 
Professor, Electronics communication, Dr.J.J.Magdum college of Engg. / Shivaji, / Jaysingpur, Maharashtra,India
 2
 
Assistant Professor, Electronics, Walchand college of Engineering/ Shivaji, Sangli, Maharashtra, India3 
 
 
Abstract: Mammography is an important research field. Mammography Image classification is an area of interest to 
most of the researchers today. The aim of this paper is to detect the Mammography image for its malignancy. Different 
methods can be used to detect the malignancy. This paper represents GLDM feature extraction method and SVM 
classifier. Experiments were conducted on MIAS database. The results show that combination of GLDM feature 
extractor with SVM classifier is found to give appropriate results. 
 
Keywords: Mammography; image classification; GLDM; SVM  
 
I. INTRODUCTION 
 
Cancer is uncontrolled growth of cells. Breast cancer is the uncontrolled growth of cells in the breast region. Breast 
cancer is the second leading cause of cancer deaths in women today. Early detection of the cancer can reduce mortality 
rate. Mammography has reported cancer detection rate of 70-90% which means 10-30% of breast cancers are missed 
with mammography [1].Early detection of breast cancer can be achieved using Digital Mammography, typically 
through detection of Characteristics masses and/or micro calcifications .A mammogram is an x-ray of the breast tissue 
which is designed to identify abnormalities. Studies have shown that radiologists can miss the detection of a significant 
proportion of abnormalities in addition to having high rates of false positives .Therefore, it would be valuable to 
develop a computer aided   method for mass/tumour classification based on extracted features from the Region of 
Interest (ROI) in mammograms [3]. Pattern recognition in image processing requires the extraction of features from 
regions of the image, and the processing of these features with a pattern recognition algorithm. Features are nothing but 
observable patterns in the image which gives some information about image. For every pattern classification problem, 
the most important stage is Feature Extraction. The accuracy of the classification depends on the Feature Extraction 
stage.  The motto behind computer aided analysis is not to replace the Radiologists but to have a second opinion and 
thus provide an efficient support in decision making process of the radiologist. Much research has been done in 
mammography towards detecting one or more abnormal structures: circumscribed masses [5], speculated lesions [6] 
and micro-calcifications [4].Other researchers have focused on classifying the breast lesions as benign or malignant. 
There are different feature descriptors such as GLDM, (Gray Level Difference Method), LBP (Local Binary Patterns), 
GLRLM(Grey level Run Length Method),Harralick, Gabor texture features  and there are classification methods such 
as SVM,C4.5,K-NN Classifier.  
In this paper we have used a GLDM feature extraction method over set of mammography images and then tested their 
performance on SVM classification algorithm. The paper is organised as follows with section 2 gives explanation about 
the pre-processing stage where as section 3 describes the feature extraction method which is used in the experiment 
Section 4 comes up with the overview of classification method. Section 5 provides explanation of experiments and 
brief discussion of results. Section 6 gives conclusion derived from this work. 
 
 
 
 
 
 
                                                                                                        
ISSN: 2319-8753 
                 
International Journal of Innovative Research in Science,  
Engineering and Technology 
(An ISO 3297: 2007 Certified Organization) 
Vol. 2, Issue 12, December 2013 
Copyright to IJIRSET                                                      www.ijirset.com                                                                            7818 
 
II.   PREPROCESSING 
 
Pre-processing stage is a step used to increase image quality of Mammograms as they are very difficult to interpret .An 
histogram equalization can be used to adjust the image contrast so that anomalies can be better emphasized.  
 
III. FEATURE EXTRACTION 
 
Feature extraction involves simplifying the amount of resources required to describe a large set of data accurately. In 
image processing, a different set of features can be used to extract the visual information from a given 
image. Because digital mammography images are specific, not all visual features can be used to correctly describe the 
relevant image patch. All classes of suspected tissue are different by their shape and tissue composition. This is why the 
most suitable visual feature descriptors for this kind of images are based on shape and texture. We can use different 
feature extraction methods and test them on variety of classifiers. We are using GLDM feature descriptor. 
GLDM 
The GLDM method calculates the Gray level difference method Probability Density functions for the given image. 
This technique is usually used for extracting statistical texture features of a digital mammogram. From each density 
functions five texture features are defined: Contrast, Angular Second moment, Entropy, Mean and Inverse Difference 
Moment.Contrast is defined as the difference in intensity between the highest and lowest intensity levels in an image 
thus measures the local variations in the grey level. Angular second moment is a measure of homogeneity. If the 
difference between gray levels over an area is low then those areas are said to be having higher ASM values. Mean it 
gives the average intensity value. Entropy is the average information per intensity source output. This parameter 
measures the disorder of an image. When the image is not texturally uniform, entropy is very large. Entropy is strongly, 
but inversely, correlated to energy. Inverse difference moment IDM measures the closeness of the distribution of 
elements in the Gray level Co-occurrence Matrix (GLCM) to the GLCM diagonal. To describe the Gray level 
difference method, let g (n,m) be the digital picture function. For any given displacement δ=(Δn, Δm),where Δn and 
Δm are integers, let gδ (n,m)=|g(n,m)-g(n+ Δn,m+ Δm)|. Let f(|δ) be the estimated probability density function 
associated with the possible values of gδ,ie, f(i|δ)=P(gδ(n,m)=i herein our possible forms of vector δ will be 
considered,(0,d),(-d,d),(d,0),(-d,d),where d is inter sample distance. we refer f(|δ) as gray level difference density 
function.  
 
IV. CLASSIFICATION METHODS 
 
There are innumerous classification methods for automated classification of samples. In this paper it’s decided to work 
with most popular classification algorithm: SVM  
Support Vector Machines  
The Support Vector machines were introduced by Vladimir Vapnik and colleagues. Support Vector machines (SVM’s) 
are a relatively new learning method used for binary classification. The basic idea is to find a hyper plane which 
separates the D-Dimensional data perfectly into its two classes. However, since example data is often not linearly 
separable, SVM’s introduce the notion of a kernel induced feature space which casts the data into a higher dimensional 
space where the data is separable. Namely, the primary goal of SVM classifiers is classification of examples that 
belong to one of two possible classes. 
However, SVM classifiers could be extended to be able to solve multiclass problems as well. One of the strategies for 
adapting binary SVM classifiers for solving multiclass problems is one-against-all (OvA) scheme. It includes 
decomposition of the M-class problem (M>2) into series of two-class problems. The basic concept is to construct M 
SVMs where the  i-th  classifier is trained to separate the class i from all other (M-1) classes. This strategy has a few 
advantages such as its precision, the possibility for easy implementation and the speed in the training phase and the 
recognition process. That is reason for its wide use. 
 
V. EXPERIMENT 
For the experiment we have used MIAS database. It is a collection of 79 images. We implemented GLDM feature 
extraction method in Mat lab V-7.1, R-12.These images are already preprocessed. After applying GLDM feature 
 
                                                                                                        
ISSN: 2319-8753 
                 
International Journal of Innovative Research in Science,  
Engineering and Technology 
(An ISO 3297: 2007 Certified Organization) 
Vol. 2, Issue 12, December 2013 
Copyright to IJIRSET                                                      www.ijirset.com                                                                            7819 
 
extractor following values are obtained. As in table 1.SVM Classifier is applied to these features which classify the 
input image as malignant or non malignant. This paper gives result for two images shown in Fig.1 and Fig.2 
 
 
                     
 
Fig.1 Input Image 1 for GLDM                      Fig.2 Input Image 2 for GLDM 
 
TABLE I 
        
FEATURES 
 
IMAGE1 
 
IMAGE2 
  
Malignant 
 
Non-Malignant 
 
Angular Second Moment 
 
0.223869 
 
0.539226 
  
Contrast 1.541076 
 
0.766746 
 
Inverse Different Moment 0.766491 
 
0.889408 
 
Mean 
 
2.748176e-001 3.370985E-001 
 Entropy 
1.821003 
 
1.179859 
  
GRAY LEVEL DIFFERENCE METHOD EXTRACTED FEATURES 
 
           
 
Output of SVM                                            Output of  SVM 
Classifier for image 1                                  Classifier for image  2 
 
 
VI.CONCLUSION 
 
Digital image processing is the most common method for early breast cancer detection. Automated analysis of these 
images is very important, since manual analysis of these images is slow, costly and inconsistent. In this paper, we made 
analysis on SVM classifier, using GLDM technique for feature extraction. According to the provided examination, we 
can say that GLDM method can be used for mass/tumor detection. 
 
 
 
 
                                                                                                        
ISSN: 2319-8753 
                 
International Journal of Innovative Research in Science,  
Engineering and Technology 
(An ISO 3297: 2007 Certified Organization) 
Vol. 2, Issue 12, December 2013 
Copyright to IJIRSET                                                      www.ijirset.com                                                                            7820 
 
 
ACKNOWLEDGMENT 
          
The authors acknowledge with thanks the equipment support of the Radiology Department of Cancer Hospital Miraj 
during the research work. 
 
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[3]M. Sampat, M. Markey, A. Bovik et al., “Computer-aided detection and diagnosis in mammography,” Handbook of image and video  
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[9] L. Martins, A. Santos, A. Silva, and A. Paiva, “Classification of normal,benign and malignant tissues using co-occurrence matrix and bayesian  
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Implementation of Classification Technique for Mammogram Image

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